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Ruitenbeek HC, Oei EHG, Visser JJ, Kijowski R. Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade. Skeletal Radiol 2024:10.1007/s00256-024-04684-6. [PMID: 38902420 DOI: 10.1007/s00256-024-04684-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 06/22/2024]
Abstract
This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.
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Affiliation(s)
- Huibert C Ruitenbeek
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Edwin H G Oei
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, 660 First Avenue, 3rd Floor, New York, NY, 10016, USA.
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Li KY, Weng JJ, Li HL, Ye HB, Xiang JW, Tian NF. Development of a Deep-Learning Model for Diagnosing Lumbar Spinal Stenosis Based on CT Images. Spine (Phila Pa 1976) 2024; 49:884-891. [PMID: 38112156 DOI: 10.1097/brs.0000000000004903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/01/2023] [Indexed: 12/20/2023]
Abstract
STUDY DESIGN Retrospective study. OBJECTIVES This study aimed to develop an initial deep-learning (DL) model based on computerized tomography (CT) scans for diagnosing lumbar spinal stenosis. SUMMARY OF BACKGROUND DATA Magnetic resonance imaging is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional coverage. Using DL models to improve the accuracy of CT diagnosis can effectively reduce missed diagnoses and misdiagnoses in clinical practice. MATERIALS AND METHODS Axial lumbar spine CT scans obtained between March 2022 and September 2023 were included. The data set was divided into a training set (62.3%), a validation set (22.9%), and a control set (14.8%). All data were labeled by two spine surgeons using the widely accepted grading system for lumbar spinal stenosis. The training and validation sets were used to annotate the regions of interest by the two spine surgeons. First, a region of interest detection model and a convolutional neural network classifier were trained using the training set. After training, the model was preliminarily evaluated using a validation set. Finally, the performance of the DL model was evaluated on the control set, and a comparison was made between the model and the classification performance of specialists with varying levels of experience. RESULTS The central stenosis grading accuracies of DL Model Version 1 and DL Model Version 2 were 88% and 83%, respectively. The lateral recess grading accuracies of DL Model Version 1 and DL Model Version 2 were 75% and 71%, respectively. CONCLUSIONS Our preliminarily developed DL system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application.
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Affiliation(s)
- Kai-Yu Li
- Department of Spine Surgery, Zhejiang Spine Research Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
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Awan KM, Goncalves Filho ALM, Tabari A, Applewhite BP, Lang M, Lo WC, Sellers R, Kollasch P, Clifford B, Nickel D, Husseni J, Rapalino O, Schaefer P, Cauley S, Huang SY, Conklin J. Diagnostic evaluation of deep learning accelerated lumbar spine MRI. Neuroradiol J 2024; 37:323-331. [PMID: 38195418 PMCID: PMC11138337 DOI: 10.1177/19714009231224428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND AND PURPOSE Deep learning (DL) accelerated MR techniques have emerged as a promising approach to accelerate routine MR exams. While prior studies explored DL acceleration for specific lumbar MRI sequences, a gap remains in comprehending the impact of a fully DL-based MRI protocol on scan time and diagnostic quality for routine lumbar spine MRI. To address this, we assessed the image quality and diagnostic performance of a DL-accelerated lumbar spine MRI protocol in comparison to a conventional protocol. METHODS We prospectively evaluated 36 consecutive outpatients undergoing non-contrast enhanced lumbar spine MRIs. Both protocols included sagittal T1, T2, STIR, and axial T2-weighted images. Two blinded neuroradiologists independently reviewed images for foraminal stenosis, spinal canal stenosis, nerve root compression, and facet arthropathy. Grading comparison employed the Wilcoxon signed rank test. For the head-to-head comparison, a 5-point Likert scale to assess image quality, considering artifacts, signal-to-noise ratio (SNR), anatomical structure visualization, and overall diagnostic quality. We applied a 15% noninferiority margin to determine whether the DL-accelerated protocol was noninferior. RESULTS No significant differences existed between protocols when evaluating foraminal and spinal canal stenosis, nerve compression, or facet arthropathy (all p > .05). The DL-spine protocol was noninferior for overall diagnostic quality and visualization of the cord, CSF, intervertebral disc, and nerve roots. However, it exhibited reduced SNR and increased artifact perception. Interobserver reproducibility ranged from moderate to substantial (κ = 0.50-0.76). CONCLUSION Our study indicates that DL reconstruction in spine imaging effectively reduces acquisition times while maintaining comparable diagnostic quality to conventional MRI.
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Affiliation(s)
- Komal M Awan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | | | - Azadeh Tabari
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | - Brooks P Applewhite
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | - Min Lang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | | | | | | | | | | | - Jad Husseni
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | - Otto Rapalino
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | - Pamela Schaefer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
| | | | - Susie Y Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, USA
| | - John Conklin
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard Medical School, USA
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Zhang D, Du J, Shi J, Zhang Y, Jia S, Liu X, Wu Y, An Y, Zhu S, Pan D, Zhang W, Zhang Y, Feng S. A fully automatic MRI-guided decision support system for lumbar disc herniation using machine learning. JOR Spine 2024; 7:e1342. [PMID: 38817341 PMCID: PMC11137648 DOI: 10.1002/jsp2.1342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Background Normalized decision support system for lumbar disc herniation (LDH) will improve reproducibility compared with subjective clinical diagnosis and treatment. Magnetic resonance imaging (MRI) plays an essential role in the evaluation of LDH. This study aimed to develop an MRI-based decision support system for LDH, which evaluates lumbar discs in a reproducible, consistent, and reliable manner. Methods The research team proposed a system based on machine learning that was trained and tested by a large, manually labeled data set comprising 217 patients' MRI scans (3255 lumbar discs). The system analyzes the radiological features of identified discs to diagnose herniation and classifies discs by Pfirrmann grade and MSU classification. Based on the assessment, the system provides clinical advice. Results Eventually, the accuracy of the diagnosis process reached 95.83%. An 83.5% agreement was observed between the system's prediction and the ground-truth in the Pfirrmann grade. In the case of MSU classification, 95.0% precision was achieved. With the assistance of this system, the accuracy, interpretation efficiency and interrater agreement among surgeons were improved substantially. Conclusion This system showed considerable accuracy and efficiency, and therefore could serve as an objective reference for the diagnosis and treatment procedure in clinical practice.
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Affiliation(s)
- Di Zhang
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Jiawei Du
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Jiaxiao Shi
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Yundong Zhang
- Beijing Longwood Valley CompanyBeijingPeople's Republic of China
| | - Siyue Jia
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Xingyu Liu
- Beijing Longwood Valley CompanyBeijingPeople's Republic of China
| | - Yu Wu
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Yicheng An
- Beijing Longwood Valley CompanyBeijingPeople's Republic of China
| | - Shibo Zhu
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Dayu Pan
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
| | - Wei Zhang
- School of Control Science and Engineering, Shandong UniversityJinanPeople's Republic of China
| | - Yiling Zhang
- Beijing Longwood Valley CompanyBeijingPeople's Republic of China
| | - Shiqing Feng
- Department of OrthopaedicsTianjin Medical University General HospitalTianjinPeople's Republic of China
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Liu RW, Ong W, Makmur A, Kumar N, Low XZ, Shuliang G, Liang TY, Ting DFK, Tan JH, Hallinan JTPD. Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs-A Systematic Review. Bioengineering (Basel) 2024; 11:484. [PMID: 38790351 PMCID: PMC11117497 DOI: 10.3390/bioengineering11050484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/24/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology.
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Affiliation(s)
- Ren Wei Liu
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore (D.F.K.T.)
| | - Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore (D.F.K.T.)
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore (D.F.K.T.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore (D.F.K.T.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Ge Shuliang
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore (D.F.K.T.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Tan Yi Liang
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore (D.F.K.T.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Dominic Fong Kuan Ting
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore (D.F.K.T.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore (D.F.K.T.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Xu Y, Zheng S, Tian Q, Kou Z, Li W, Xie X, Wu X. Deep Learning Model for Grading and Localization of Lumbar Disc Herniation on Magnetic Resonance Imaging. J Magn Reson Imaging 2024. [PMID: 38676436 DOI: 10.1002/jmri.29403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Methods for grading and localization of lumbar disc herniation (LDH) on MRI are complex, time-consuming, and subjective. Utilizing deep learning (DL) models as assistance would mitigate such complexities. PURPOSE To develop an interpretable DL model capable of grading and localizing LDH. STUDY TYPE Retrospective. SUBJECTS 1496 patients (M/F: 783/713) were evaluated, and randomly divided into training (70%), validation (10%), and test (20%) sets. FIELD STRENGTH/SEQUENCE 1.5T MRI for axial T2-weighted sequences (spin echo). ASSESSMENT The training set was annotated by three spinal surgeons using the Michigan State University classification to train the DL model. The test set was annotated by a spinal surgery expert (as ground truth labels), and two spinal surgeons (comparison with the trained model). An external test set was employed to evaluate the generalizability of the DL model. STATISTICAL TESTS Calculated intersection over union (IoU) for detection consistency, utilized Gwet's AC1 to assess interobserver agreement, and evaluated model performance based on sensitivity and specificity, with statistical significance set at P < 0.05. RESULTS The DL model achieved high detection consistency in both the internal test dataset (grading: mean IoU 0.84, recall 99.6%; localization: IoU 0.82, recall 99.5%) and external test dataset (grading: 0.72, 98.0%; localization: 0.71, 97.6%). For internal testing, the DL model (grading: 0.81; localization: 0.76), Rater 1 (0.88; 0.82), and Rater 2 (0.86; 0.83) demonstrated results highly consistent with the ground truth labels. The overall sensitivity of the DL model was 87.0% for grading and 84.0% for localization, while the specificity was 95.5% and 94.4%. For external testing, the DL model showed an appreciable decrease in consistency (grading: 0.69; localization: 0.66), sensitivity (77.2%; 76.7%), and specificity (92.3%; 91.8%). DATA CONCLUSION The classification capabilities of the DL model closely resemble those of spinal surgeons. For future improvement, enriching the diversity of cases could enhance the model's generalization. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Yefu Xu
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shijie Zheng
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Qingyi Tian
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhuoyan Kou
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Wenqing Li
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xinhui Xie
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xiaotao Wu
- Department of Spine Surgery, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
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Li M, Zheng H, Koh JC, Choe GY, Choi EJ, Nahm FS, Lee PB. Development of a Deep Learning Model for the Analysis of Dorsal Root Ganglion Chromatolysis in Rat Spinal Stenosis. J Pain Res 2024; 17:1369-1380. [PMID: 38600989 PMCID: PMC11005935 DOI: 10.2147/jpr.s444055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/20/2024] [Indexed: 04/12/2024] Open
Abstract
Objective To create a deep learning (DL) model that can accurately detect and classify three distinct types of rat dorsal root ganglion neurons: normal, segmental chromatolysis, and central chromatolysis. The DL model has the potential to improve the efficiency and precision of neuron classification in research related to spinal injuries and diseases. Methods H&E slide images were divided into an internal training set (80%) and a test set (20%). The training dataset was labeled by two pathologists using pre-defined grades. Using this dataset, a two-component DL model was developed with the first component being a convolutional neural network (CNN) that was trained to detect the region of interest (ROI) and the second component being another CNN used for classification. Results A total of 240 lumbar dorsal root ganglion (DRG) pathology slide images from rats were analyzed. The internal testing results showed an accuracy of 93.13%, and the external dataset testing demonstrated an accuracy of 93.44%. Conclusion The DL model demonstrated a level of agreement comparable to that of pathologists in detecting and classifying normal and segmental chromatolysis neurons, although its agreement was slightly lower for central chromatolysis neurons. Significance: DL in improving the accuracy and efficiency of pathological analysis suggests that it may have a role in enhancing medical decision-making.
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Affiliation(s)
- Meihui Li
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Haiyan Zheng
- Department of Anesthesiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
| | - Jae Chul Koh
- Department of Anesthesiology and Pain Medicine, Korea University College of Medicine, Seoul, Korea
| | - Ghee Young Choe
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Eun Joo Choi
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Francis Sahngun Nahm
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Pyung Bok Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
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Qu Z, Deng B, Sun W, Yang R, Feng H. A Convolutional Neural Network for Automated Detection of Cervical Ossification of the Posterior Longitudinal Ligament using Magnetic Resonance Imaging. Clin Spine Surg 2024; 37:E106-E112. [PMID: 37941120 DOI: 10.1097/bsd.0000000000001547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 10/03/2023] [Indexed: 11/10/2023]
Abstract
STUDY DESIGN Retrospective cohort study. OBJECTIVE We aimed to develop and validate a convolutional neural network (CNN) model to distinguish between cervical ossification of posterior longitudinal ligament (OPLL) and multilevel degenerative spinal stenosis using Magnetic Resonance Imaging (MRI) and to compare the diagnostic ability with spine surgeons. SUMMARY OF BACKGROUND DATA Some artificial intelligence models have been applied in spinal image analysis and many of promising results were obtained; however, there was still no study attempted to develop a deep learning model in detecting cervical OPLL using MRI images. MATERIALS AND METHODS In this retrospective study, 272 cervical OPLL and 412 degenerative patients underwent surgical treatment were enrolled and divided into the training (513 cases) and test dataset (171 cases). CNN models applying ResNet architecture with 34, 50, and 101 layers of residual blocks were constructed and trained with the sagittal MRI images from the training dataset. To evaluate the performance of CNN, the receiver operating characteristic curves of 3 ResNet models were plotted and the area under the curve were calculated on the test dataset. The accuracy, sensitivity, and specificity of the diagnosis by the CNN were calculated and compared with 3 senior spine surgeons. RESULTS The diagnostic accuracies of our ResNet34, ResNet50, and ResNet101 models were 92.98%, 95.32%, and 97.66%, respectively; the area under the curve of receiver operating characteristic curves of these models were 0.914, 0.942, and 0.971, respectively. The accuracies and specificities of ResNet50 and ResNet101 models were significantly higher than all spine surgeons; for the sensitivity, ResNet101 model achieved better values than that of the 2 surgeons. CONCLUSION The performance of our ResNet model in differentiating cervical OPLL from degenerative spinal stenosis using MRI is promising, better results were achieved with more layers of residual blocks applied.
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Affiliation(s)
- Zhe Qu
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Bin Deng
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Wei Sun
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
| | - Ranran Yang
- Xuzhou Medical University
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hu Feng
- Department of Spine Surgery, The Affiliated Hospital of Xuzhou Medical University
- Xuzhou Medical University
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Lee Y, Ziad Issa T, Mazmudar AS, Tarawneh OH, Toci GR, Lambrechts MJ, DiDomenico EJ, Kwak D, Becsey AN, Henry TW, Haider AA, Larkin CJ, Kaye ID, Kurd MF, Canseco JA, Hilibrand AS, Vaccaro AR, Kepler CK, Schroeder GD. Radiology Reports Do Not Accurately Portray the Severity of Cervical Neural Foraminal Stenosis. Clin Spine Surg 2024:01933606-990000000-00278. [PMID: 38490967 DOI: 10.1097/bsd.0000000000001603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/22/2024] [Indexed: 03/18/2024]
Abstract
STUDY DESIGN Retrospective study. OBJECTIVE (1) To compare cervical magnetic resonance imaging (MRI) radiology reports to a validated grading system for cervical foraminal stenosis (FS) and (2) to evaluate whether the severity of cervical neural FS on MRI correlates to motor weakness or patient-reported outcomes. BACKGROUND Radiology reports of cervical spine MRI are often reviewed to assess the degree of neural FS. However, research looking at the association between these reports and objective MRI findings, as well as clinical symptoms, is lacking. PATIENTS AND METHODS We retrospectively identified all adult patients undergoing primary 1 or 2-level anterior cervical discectomy and fusion at a single academic center for an indication of cervical radiculopathy. Preoperative MRI was assessed for neural FS severity using the grading system described by Kim and colleagues for each level of fusion, as well as adjacent levels. Neural FS severity was recorded from diagnostic radiologist MRI reports. Motor weakness was defined as an examination grade <4/5 on the final preoperative encounter. Regression analysis was conducted to evaluate whether the degree of FS by either classification was related to patient-reported outcome measure severity. RESULTS A total of 283 patients were included in the study, and 998 total levels were assessed. There were significant differences between the MRI grading system and the assessment by radio-logists (P< 0.001). In levels with moderate stenosis, 28.9% were classified as having no stenosis by radiology. In levels with severe stenosis, 29.7% were classified as having mild-moderate stenosis or less. Motor weakness was found similarly often in levels of moderate or severe stenosis (6.9% and 9.2%, respectively). On regression analysis, no associations were found between baseline patient-reported outcome measures and stenosis severity assessed by radiologists or MRI grading systems. CONCLUSION Radiology reports on the severity of cervical neural FS are not consistent with a validated MRI grading system. These radiology reports underestimated the severity of neural foraminal compression and may be inappropriate when used for clinical decision-making. LEVEL OF EVIDENCE Level III.
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Affiliation(s)
- Yunsoo Lee
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Tariq Ziad Issa
- Department of Orthopaedic Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Aditya S Mazmudar
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Omar H Tarawneh
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Gregory R Toci
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Mark J Lambrechts
- Department of Orthopaedic Surgery, School of Medicine, Washington University, St. Louis, MO
| | - Eric J DiDomenico
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Daniel Kwak
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Alexander N Becsey
- Department of Orthopaedic Surgery, College of Medicine, Drexel University, Philadelphia, PA
| | - Tyler W Henry
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Ameer A Haider
- Department of Orthopaedic Surgery, School of Medicine, Washington University, St. Louis, MO
| | - Collin J Larkin
- Department of Orthopaedic Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Ian David Kaye
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Mark F Kurd
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Jose A Canseco
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Alan S Hilibrand
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Alexander R Vaccaro
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Christopher K Kepler
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
| | - Gregory D Schroeder
- Department of Orthopaedic Surgery, Rothman Institute, Thomas Jefferson University, Philadelphia, PA
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Miao Q, Wang X, Cui J, Zheng H, Xie Y, Zhu K, Chai R, Jiang Y, Feng D, Zhang X, Shi F, Tan X, Fan G, Liang K. Artificial intelligence to predict T4 stage of pancreatic ductal adenocarcinoma using CT imaging. Comput Biol Med 2024; 171:108125. [PMID: 38340439 DOI: 10.1016/j.compbiomed.2024.108125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND The accurate assessment of T4 stage of pancreatic ductal adenocarcinoma (PDAC) has consistently presented a considerable difficulty for radiologists. This study aimed to develop and validate an automated artificial intelligence (AI) pipeline for the prediction of T4 stage of PDAC using contrast-enhanced CT imaging. METHODS The data were obtained retrospectively from consecutive patients with surgically resected and pathologically proved PDAC at two institutions between July 2017 and June 2022. Initially, a deep learning (DL) model was developed to segment PDAC. Subsequently, radiomics features were extracted from the automatically segmented region of interest (ROI), which encompassed both the tumor region and a 3 mm surrounding area, to construct a predictive model for determining T4 stage of PDAC. The assessment of the models' performance involved the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS The study encompassed a cohort of 509 PDAC patients, with a median age of 62 years (interquartile range: 55-67). The proportion of patients in T4 stage within the model was 16.9%. The model achieved an AUC of 0.849 (95% CI: 0.753-0.940), a sensitivity of 0.875, and a specificity of 0.728 in predicting T4 stage of PDAC. The performance of the model was determined to be comparable to that of two experienced abdominal radiologists (AUCs: 0.849 vs. 0.834 and 0.857). CONCLUSION The automated AI pipeline utilizing tumor and peritumor-related radiomics features demonstrated comparable performance to that of senior abdominal radiologists in predicting T4 stage of PDAC.
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Affiliation(s)
- Qi Miao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xuechun Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Jingjing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Bejing, China
| | - Haoxin Zheng
- Department of Computer Science, University of California, Los Angeles, USA
| | - Yan Xie
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Kexin Zhu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Ruimei Chai
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yuanxi Jiang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Dongli Feng
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xin Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiaodong Tan
- Department of General Surgery/Pancreatic and Thyroid Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China.
| | - Keke Liang
- Department of General Surgery/Pancreatic and Thyroid Surgery, Shengjing Hospital of China Medical University, Shenyang, China.
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11
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Bharadwaj UU, Chin CT, Majumdar S. Practical Applications of Artificial Intelligence in Spine Imaging: A Review. Radiol Clin North Am 2024; 62:355-370. [PMID: 38272627 DOI: 10.1016/j.rcl.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, which is essential for positive patient outcomes. This review explores AI algorithms, techniques, and applications in spine imaging, highlighting diagnostic impact and challenges with future directions for integrating AI into spine imaging workflow.
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Affiliation(s)
- Upasana Upadhyay Bharadwaj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA
| | - Cynthia T Chin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 505 Parnassus Avenue, Box 0628, San Francisco, CA 94143, USA.
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 1700 4th Street, Byers Hall, Suite 203, Room 203D, San Francisco, CA 94158, USA
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12
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Tumko V, Kim J, Uspenskaia N, Honig S, Abel F, Lebl DR, Hotalen I, Kolisnyk S, Kochnev M, Rusakov A, Mourad R. A neural network model for detection and classification of lumbar spinal stenosis on MRI. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2024; 33:941-948. [PMID: 38150003 DOI: 10.1007/s00586-023-08089-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/30/2023] [Accepted: 12/04/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVES To develop a three-stage convolutional neural network (CNN) approach to segment anatomical structures, classify the presence of lumbar spinal stenosis (LSS) for all 3 stenosis types: central, lateral recess and foraminal and assess its severity on spine MRI and to demonstrate its efficacy as an accurate and consistent diagnostic tool. METHODS The three-stage model was trained on 1635 annotated lumbar spine MRI studies consisting of T2-weighted sagittal and axial planes at each vertebral level. Accuracy of the model was evaluated on an external validation set of 150 MRI studies graded on a scale of absent, mild, moderate or severe by a panel of 7 radiologists. The reference standard for all types was determined by majority voting and in case of disagreement, adjudicated by an external radiologist. The radiologists' diagnoses were then compared to the diagnoses of the model. RESULTS The model showed comparable performance to the radiologist average both in terms of the determination of presence/absence of LSS as well as severity classification, for all 3 stenosis types. In the case of central canal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.971, 0.864, 0.963) for binary (presence/absence) classification compared to the radiologist average of (0.786, 0.899, 0.842). For lateral recess stenosis, the sensitivity, specificity and AUROC of the CNN were (0.853, 0.787, 0.907) compared to the radiologist average of (0.713, 0.898, 805). For foraminal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.942, 0.844, 0.950) compared to the radiologist average of (0.879, 0.877, 0.878). Multi-class severity classifications showed similarly comparable statistics. CONCLUSIONS The CNN showed comparable performance to radiologist subspecialists for the detection and classification of LSS. The integration of neural network models in the detection of LSS could bring higher accuracy, efficiency, consistency, and post-hoc interpretability in diagnostic practices.
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Affiliation(s)
- Vladislav Tumko
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Jack Kim
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA.
| | - Natalia Uspenskaia
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Shaun Honig
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Frederik Abel
- Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Darren R Lebl
- Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Irene Hotalen
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | | | - Mikhail Kochnev
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Andrej Rusakov
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Raphaël Mourad
- University of Toulouse, 118 Rte de Narbonne, 31062, Toulouse, France.
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Bogdanovic S, Staib M, Schleiniger M, Steiner L, Schwarz L, Germann C, Sutter R, Fritz B. AI-Based Measurement of Lumbar Spinal Stenosis on MRI: External Evaluation of a Fully Automated Model. Invest Radiol 2024:00004424-990000000-00200. [PMID: 38426719 DOI: 10.1097/rli.0000000000001070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
OBJECTIVES The aim of this study was to clinically validate a fully automated AI model for magnetic resonance imaging (MRI)-based quantifications of lumbar spinal canal stenosis. MATERIALS AND METHODS This retrospective study included lumbar spine MRI of 100 consecutive clinical patients (56 ± 17 years; 43 females, 57 males) performed on clinical 1.5 (51 examinations) and 3 T MRI scanners (49 examinations) with heterogeneous clinical imaging protocols. The AI model performed segmentations of the thecal sac on axial T2-weighted sequences. Based on these segmentations, the anteroposterior (AP) and mediolateral (ML) distance, and the area of the thecal sac were measured in a fully automated manner. For comparison, 2 fellowship-trained musculoskeletal radiologists performed the same segmentations and measurements independently. Statistics included 1-sample t tests, the intraclass correlation coefficient (ICC), Bland-Altman plots, and Dice coefficients. A P value of <0.05 was considered statistically significant. RESULTS The average measurements of the AI model, reader 1, and reader 2 were 194 ± 72 mm2, 181 ± 71 mm2, and 179 ± 70 mm2 for thecal sac area, 13 ± 3.3 mm, 12.6 ± 3.3 mm, and 12.6 ± 3.2 mm for AP distance, and 19.5 ± 3.9 mm, 20 ± 4.3 mm, and 19.4 ± 4 mm for ML distance, respectively. Significant differences existed for all pairwise comparisons, besides reader 1 versus AI model for the ML distance and reader 1 versus reader 2 for the AP distance (P = 0.1 and P = 0.21, respectively). The pairwise mean absolute errors among reader 1, reader 2, and the AI model ranged from 0.59 mm and 0.75 mm for the AP distance, from 1.16 mm to 1.37 mm for the ML distance, and from 7.9 mm2 to 15.54 mm2 for the thecal sac area. Pairwise ICCs among reader 1, reader 2, and the AI model ranged from 0.91 and 0.94 for the AP distance and from 0.86 to 0.9 for the ML distance without significant differences. For the thecal sac area, the pairwise ICC between both readers and the AI model of 0.97 each was slightly, but significantly lower than the ICC between reader 1 and reader 2 of 0.99. Similarly, the Dice coefficient and Hausdorff distance between both readers and the AI model were significantly lower than the values between reader 1 and reader 2, overall ranging from 0.93 to 0.95 for the Dice coefficients and 1.1 to 1.44 for the Hausdorff distances. CONCLUSIONS The investigated AI model is reliable for assessing the AP and the ML thecal sac diameters with human level accuracies. The small differences for measurement and segmentation of the thecal sac area between the AI model and the radiologists are likely within a clinically acceptable range.
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Affiliation(s)
- Sanja Bogdanovic
- From the Radiology, Balgrist University Hospital, Zurich, Switzerland (S.B., C.G., R.S., B.F.); Faculty of Medicine, University of Zurich, Zurich, Switzerland (S.B., C.G., R.S., B.F.); and ScanDiags AG, Zurich, Switzerland (M. Staib, M. Schleiniger, L. Steiner, and L. Schwarz)
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Hashimoto E, Maki S, Ochiai N, Ise S, Inagaki K, Hiraoka Y, Hattori F, Ohtori S. Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning. J Shoulder Elbow Surg 2024:S1058-2746(24)00076-4. [PMID: 38311106 DOI: 10.1016/j.jse.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 12/08/2023] [Accepted: 12/17/2023] [Indexed: 02/06/2024]
Abstract
BACKGROUND The diagnosis of rotator cuff tears (RCTs) using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recognition. We aimed to evaluate the diagnostic performance of artificial intelligence (a deep learning algorithm; a convolutional neural network) to detect and classify RCTs using shoulder radiographs, and compare its diagnostic performance with that of orthopedic surgeons. METHODS A total of 1169 plain shoulder anteroposterior radiographs (1 image per shoulder) were included in the total dataset and divided into four groups: intact, small, medium, and large to massive tear groups. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating curve were measured for the detection of RCTs through binary classification. The average accuracy, recall, precision, and F1-score were divided into four groups by cuff tear size for multiclass classification. RESULTS The convolutional neural network demonstrated a high performance, with 92% sensitivity, 69% specificity, 86% accuracy, and an area under the receiver operating curve of 0.88 for the detection of RCTs. The average accuracy, recall, precision, and F1-score of the convolutional neural network for classification were 60%, 0.42, 0.49, and 0.45, respectively. The accuracy of the convolutional neural network for the detection and classification of RCTs was significantly better than that of orthopedic surgeons. CONCLUSION The convolutional neural network demonstrated the diagnostic ability to detect and classify RCTs using plain shoulder radiographs, and the diagnostic performance exhibited equal to superior accuracy when compared with those of shoulder experts.
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Affiliation(s)
- Eiko Hashimoto
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan.
| | - Satoshi Maki
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Nobuyasu Ochiai
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Shohei Ise
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Kenta Inagaki
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Yu Hiraoka
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Fumiya Hattori
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
| | - Seiji Ohtori
- Department of Orthopedics Surgery, Chiba University, Chiba, Japan
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15
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Xie J, Yang Y, Jiang Z, Zhang K, Zhang X, Lin Y, Shen Y, Jia X, Liu H, Yang S, Jiang Y, Ma L. MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study. Front Physiol 2024; 14:1281506. [PMID: 38235385 PMCID: PMC10791783 DOI: 10.3389/fphys.2023.1281506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/24/2023] [Indexed: 01/19/2024] Open
Abstract
Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration. Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis. Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05). Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management.
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Affiliation(s)
- Jun Xie
- Information Technology Center, West China Hospital of Sichuan University, Chengdu, China
- Information Technology Center, Sanya People’s Hospital, Sanya, China
| | - Yi Yang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zekun Jiang
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
- West China Biomedical Big Data Center, Sichuan University, Chengdu, Sichuan, China
| | - Kerui Zhang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiang Zhang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuheng Lin
- West China Biomedical Big Data Center, Sichuan University, Chengdu, Sichuan, China
| | - Yiwei Shen
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xuehai Jia
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hao Liu
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shaofen Yang
- Cadre Health Section, Hezhou People’s Hospital, Hezhou, Guangxi, China
| | - Yang Jiang
- Department of Orthopedic Spine, The Second Affiliated Hospital of Chengdu Medical College (China National Nuclear Corporation 416 Hospital), Chengdu, Sichuan, China
| | - Litai Ma
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Abel F, Garcia E, Andreeva V, Nikolaev NS, Kolisnyk S, Sarbaev R, Novikov I, Kozinchenko E, Kim J, Rusakov A, Mourad R, Lebl DR. An Artificial Intelligence-Based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire. World Neurosurg 2024; 181:e953-e962. [PMID: 37952887 DOI: 10.1016/j.wneu.2023.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/04/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiologic characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires. METHODS We evaluated multiple machine learning (ML) models to determine the likelihood of LSS using a self-reported questionnaire in patients experiencing low back pain and/or numbness in the legs. The questionnaire was built from peer-reviewed literature and a multidisciplinary panel of experts. Random forest, lasso logistic regression, support vector machine, gradient boosting trees, deep neural networks, and automated machine learning models were trained and performance metrics were compared. RESULTS Data from 4827 patients (4690 patients without LSS: mean age 62.44, range 27-84 years, 62.8% females, and 137 patients with LSS: mean age 50.59, range 30-71 years, 59.9% females) were retrospectively collected. Among the evaluated models, the random forest model demonstrated the highest predictive accuracy with an area under the receiver operating characteristic curve (AUROC) between model prediction and LSS diagnosis of 0.96, a sensitivity of 0.94, a specificity of 0.88, a balanced accuracy of 0.91, and a Cohen's kappa of 0.85. CONCLUSIONS Our results indicate that ML can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy. Implementation of standardized and intelligence-automated workflow may serve as a supportive diagnostic tool to streamline patient management and potentially lower health care costs.
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Affiliation(s)
- Frederik Abel
- Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA
| | | | - Vera Andreeva
- Federal State Budgetary Institution, Federal Center for Traumatology, Orthopedics and Arthroplasty, Ministry of Health of the Russian Federation, Cheboksary, Russia
| | - Nikolai S Nikolaev
- Federal State Budgetary Institution, Federal Center for Traumatology, Orthopedics and Arthroplasty, Ministry of Health of the Russian Federation, Cheboksary, Russia; Federal State Budgetary Educational Institution of Higher Education, Chuvash State University named after I.N. Ulyanov, Cheboksary, Russia
| | - Serhii Kolisnyk
- Department of Physical and Rehabilitation Medicine, Vinnitsa National Medical University, Vinnytsia, Ukraine
| | | | | | | | - Jack Kim
- Remedy Logic, New York, New York, USA
| | | | - Raphael Mourad
- University of Toulouse, CNRS, UPS, Toulouse, France; Remedy Logic, New York, New York, USA.
| | - Darren R Lebl
- Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA
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Zerunian M, Pucciarelli F, Caruso D, De Santis D, Polici M, Masci B, Nacci I, Del Gaudio A, Argento G, Redler A, Laghi A. Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol. Skeletal Radiol 2024; 53:151-159. [PMID: 37369725 PMCID: PMC10661795 DOI: 10.1007/s00256-023-04390-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023]
Abstract
OBJECTIVE The objective of this study is to prospectively compare quantitative and subjective image quality, scanning time, and diagnostic confidence between a new deep learning-based reconstruction(DLR) algorithm and standard MRI protocol of lumbar spine. MATERIALS AND METHODS Eighty healthy volunteers underwent 1.5T MRI examination of lumbar spine from September 2021 to May 2023. Protocol acquisition comprised sagittal T1- and T2-weighted fast spin echo and short-tau inversion recovery images and axial multislices T2-weighted fast spin echo images. All sequences were acquired with both DLR algorithm and standard protocols. Two radiologists, blinded to the reconstruction technique, performed quantitative and qualitative image quality analysis in consensus reading; diagnostic confidence was also assessed. Quantitative image quality analysis was assessed by calculating signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative image quality analysis and diagnostic confidence were assessed with a five-point Likert scale. Scanning times were also compared. RESULTS DLR SNR was higher in all sequences (all p<0.001). CNR of the DLR was superior to conventional dataset only for axial and sagittal T2-weighted fast spin echo images (p<0.001). Qualitative analysis showed DLR had higher overall quality in all sequences (all p<0.001), with an inter-rater agreement of 0.83 (0.78-0.86). DLR total protocol scanning time was lower compared to standard protocol (6:26 vs 12:59 min, p<0.001). Diagnostic confidence for DLR algorithm was not inferior to standard protocol. CONCLUSION DLR applied to 1.5T MRI is a feasible method for lumbar spine imaging providing morphologic sequences with higher image quality and similar diagnostic confidence compared with standard protocol, enabling a remarkable time saving (up to 50%).
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Affiliation(s)
- Marta Zerunian
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Francesco Pucciarelli
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Domenico De Santis
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Michela Polici
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Benedetta Masci
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Ilaria Nacci
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Antonella Del Gaudio
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Giuseppe Argento
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy
| | - Andrea Redler
- Orthopaedic Unit and Kirk Kilgour Sports Injury Centre, University of Rome "Sapienza" - Sant'Andrea University Hospital, Rome, Italy
| | - Andrea Laghi
- Department of Medical Surgical Sciences and Translational Medicine, University of Rome "Sapienza" Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189, Rome, Italy.
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Fan G, Wang D, Li Y, Xu Z, Wang H, Liu H, Liao X. Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography. Diagnostics (Basel) 2023; 14:53. [PMID: 38201362 PMCID: PMC10795799 DOI: 10.3390/diagnostics14010053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/17/2023] [Accepted: 11/29/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients. METHODS A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not. RESULTS The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis. CONCLUSIONS ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.
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Affiliation(s)
- Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Dongdong Wang
- Department of Orthopaedics, Putuo People’s Hospital, Tongji University, Shanghai 200060, China;
| | - Yufeng Li
- Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China;
| | - Zhipeng Xu
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
| | - Hong Wang
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Guangzhou 510700, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518056, China; (G.F.); (Z.X.); (H.W.)
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Yoo H, Yoo RE, Choi SH, Hwang I, Lee JY, Seo JY, Koh SY, Choi KS, Kang KM, Yun TJ. Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI. Eur Radiol 2023; 33:8656-8668. [PMID: 37498386 DOI: 10.1007/s00330-023-09918-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/28/2023] [Accepted: 05/31/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVE To compare the image quality and diagnostic performance between standard turbo spin-echo MRI and accelerated MRI with deep learning (DL)-based image reconstruction for degenerative lumbar spine diseases. MATERIALS AND METHODS Fifty patients who underwent both the standard and accelerated lumbar MRIs at a 1.5-T scanner for degenerative lumbar spine diseases were prospectively enrolled. DL reconstruction algorithm generated coarse (DL_coarse) and fine (DL_fine) images from the accelerated protocol. Image quality was quantitatively assessed in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) and qualitatively assessed using five-point visual scoring systems. The sensitivity and specificity of four radiologists for the diagnosis of degenerative diseases in both protocols were compared. RESULTS The accelerated protocol reduced the average MRI acquisition time by 32.3% as compared to the standard protocol. As compared with standard images, DL_coarse and DL_fine showed significantly higher SNRs on T1-weighted images (T1WI; both p < .001) and T2-weighted images (T2WI; p = .002 and p < 0.001), higher CNRs on T1WI (both p < 0.001), and similar CNRs on T2WI (p = .49 and p = .27). The average radiologist assessment of overall image quality for DL_coarse and DL_fine was higher on sagittal T1WI (p = .04 and p < .001) and axial T2WI (p = .006 and p = .01) and similar on sagittal T2WI (p = .90 and p = .91). Both DL_coarse and DL_fine had better image quality of cauda equina and paraspinal muscles on axial T2WI (both p = .04 for cauda equina; p = .008 and p = .002 for paraspinal muscles). Differences in sensitivity and specificity for the detection of central canal stenosis and neural foraminal stenosis between standard and DL-reconstructed images were all statistically nonsignificant (p ≥ 0.05). CONCLUSION DL-based protocol reduced MRI acquisition time without degrading image quality and diagnostic performance of readers for degenerative lumbar spine diseases. CLINICAL RELEVANCE STATEMENT The deep learning (DL)-based reconstruction algorithm may be used to further accelerate spine MRI imaging to reduce patient discomfort and increase the cost efficiency of spine MRI imaging. KEY POINTS • By using deep learning (DL)-based reconstruction algorithm in combination with the accelerated MRI protocol, the average acquisition time was reduced by 32.3% as compared with the standard protocol. • DL-reconstructed images had similar or better quantitative/qualitative overall image quality and similar or better image quality for the delineation of most individual anatomical structures. • The average radiologist's sensitivity and specificity for the detection of major degenerative lumbar spine diseases, including central canal stenosis, neural foraminal stenosis, and disc herniation, on standard and DL-reconstructed images, were similar.
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Affiliation(s)
- Hyunsuk Yoo
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Roh-Eul Yoo
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Seung Hong Choi
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea
| | - Inpyeong Hwang
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Ji Ye Lee
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
| | - June Young Seo
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Seok Young Koh
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Tae Jin Yun
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea
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20
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Wang C, Ni M, Tian S, Ouyang H, Liu X, Fan L, Dong P, Jiang L, Lang N, Yuan H. Deep learning model for measuring the sagittal Cobb angle on cervical spine computed tomography. BMC Med Imaging 2023; 23:196. [PMID: 38017414 PMCID: PMC10685593 DOI: 10.1186/s12880-023-01156-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/15/2023] [Indexed: 11/30/2023] Open
Abstract
PURPOSES To develop a deep learning (DL) model to measure the sagittal Cobb angle of the cervical spine on computed tomography (CT). MATERIALS AND METHODS Two VB-Net-based DL models for cervical vertebra segmentation and key-point detection were developed. Four-points and line-fitting methods were used to calculate the sagittal Cobb angle automatically. The average value of the sagittal Cobb angle was manually measured by two doctors as the reference standard. The percentage of correct key points (PCK), matched samples t test, intraclass correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), and Bland‒Altman plots were used to evaluate the performance of the DL model and the robustness and generalization of the model on the external test set. RESULTS A total of 991 patients were included in the internal data set, and 112 patients were included in the external data set. The PCK of the DL model ranged from 78 to 100% in the test set. The four-points method, line-fitting method, and reference standard measured sagittal Cobb angles were - 1.10 ± 18.29°, 0.30 ± 13.36°, and 0.50 ± 12.83° in the internal test set and 4.55 ± 20.01°, 3.66 ± 18.55°, and 1.83 ± 12.02° in the external test set, respectively. The sagittal Cobb angle calculated by the four-points method and the line-fitting method maintained high consistency with the reference standard (internal test set: ICC = 0.75 and 0.97; r = 0.64 and 0.94; MAE = 5.42° and 3.23°, respectively; external test set: ICC = 0.74 and 0.80, r = 0.66 and 0.974, MAE = 5.25° and 4.68°, respectively). CONCLUSIONS The DL model can accurately measure the sagittal Cobb angle of the cervical spine on CT. The line-fitting method shows a higher consistency with the doctors and a minor average absolute error.
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Affiliation(s)
- Chunjie Wang
- Department of Radiology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, China
| | - Ming Ni
- Department of Radiology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, China
| | - Shuai Tian
- Department of Radiology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, China
| | - Hanqiang Ouyang
- Department of Orthopedics, Peking University Third Hospital, Beijing, 100191, China
- Engineering Research Center of Bone and Joint Precision Medicine, Beijing, 100191, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China
| | - Xiaoming Liu
- Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, 100089, China
| | - Lianxi Fan
- United Imaging Intelligence (Beijing) Co., Ltd., Beijing, 100089, China
| | - Pei Dong
- United Imaging Intelligence (Beijing) Co., Ltd., Beijing, 100089, China
| | - Liang Jiang
- Department of Orthopedics, Peking University Third Hospital, Beijing, 100191, China
- Engineering Research Center of Bone and Joint Precision Medicine, Beijing, 100191, China
- Beijing Key Laboratory of Spinal Disease Research, Beijing, 100191, China
| | - Ning Lang
- Department of Radiology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, China.
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Flanders AE, Geis JR. NextGen Neuroradiology AI. Radiology 2023; 309:e231426. [PMID: 37987667 DOI: 10.1148/radiol.231426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Affiliation(s)
- Adam E Flanders
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Suite 1080B Main Building, Philadelphia, PA 19107 (A.E.F.); and Department of Radiology, National Jewish Health, Denver, Colo (J.R.G.)
| | - J Raymond Geis
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Suite 1080B Main Building, Philadelphia, PA 19107 (A.E.F.); and Department of Radiology, National Jewish Health, Denver, Colo (J.R.G.)
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Hallinan JTPD, Zhu L, Tan HWN, Hui SJ, Lim X, Ong BWL, Ong HY, Eide SE, Cheng AJL, Ge S, Kuah T, Lim SWD, Low XZ, Teo EC, Yap QV, Chan YH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Tan JH. A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3815-3824. [PMID: 37093263 DOI: 10.1007/s00586-023-07706-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/12/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. METHODS We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC. RESULTS Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001). CONCLUSION A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore.
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore
| | - Hui Wen Natalie Tan
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Si Jian Hui
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Xinyi Lim
- Orthopaedic Centre, Alexandra Hospital, 378 Alexandra Road, Singapore, 159964, Singapore
| | - Bryan Wei Loong Ong
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Han Yang Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Sterling Ellis Eide
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Amanda J L Cheng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Shi Wei Desmond Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore, 117597, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore, 117597, Singapore
| | - Naresh Kumar
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Jiong Hao Tan
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
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Wu Y, Chen X, Dong F, He L, Cheng G, Zheng Y, Ma C, Yao H, Zhou S. Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023:10.1007/s00586-023-07937-5. [PMID: 37787781 DOI: 10.1007/s00586-023-07937-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 04/03/2023] [Accepted: 08/30/2023] [Indexed: 10/04/2023]
Abstract
PURPOSE To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance. METHODS A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland-Altman plot were used to evaluate the performance of the cascade HRNet model. RESULTS The PCK of the cascaded HRNet model was 97.9-100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989-0.999, R = 0.991-0.999, MAE = 0.63-1.65, MSE = 0.61-4.06, RMSE = 0.78-2.01). CONCLUSION The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application.
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Affiliation(s)
- Yuhua Wu
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Xiaofei Chen
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China
| | - Fuwen Dong
- Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China
| | - Linyang He
- Hangzhou Jianpei Technology Company Ltd, Hangzhou, 311200, Zhejiang, China
| | - Guohua Cheng
- Hangzhou Jianpei Technology Company Ltd, Hangzhou, 311200, Zhejiang, China
| | - Yuwen Zheng
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Chunyu Ma
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China
| | - Hongyan Yao
- Department of Radiology, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou, 730000, Gansu, China
| | - Sheng Zhou
- Department of Radiology, Gansu Provincial Hospital, No. 204, Donggang West Road, Lanzhou, 730000, Gansu, China.
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Xu W, Liu W, Zhong F, Peng Y, Liu X, Yu L. Efficacy of OLIF combined with pedicle screw internal fixation for lumbar spinal stenosis on spinal canal changes before and after surgery. J Orthop Surg Res 2023; 18:724. [PMID: 37749636 PMCID: PMC10519078 DOI: 10.1186/s13018-023-04209-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 09/14/2023] [Indexed: 09/27/2023] Open
Abstract
PURPOSE The purpose of the study was to evaluate the efficacy of OLIF combined with pedicle screw internal fixation in the treatment of lumbar spinal stenosis by assessing the changes in spinal canal before and after surgery. METHODS In this retrospective study, we included sixteen patients who underwent a combination of single-segment OLIF and pedicle screw internal fixation for the treatment of lumbar spinal stenosis at the Affiliated Hospital of Jiangxi University of Chinese Medicine between February 2018 and August 2022. The patients' pre- and post-operative data were compared. Intraoperative bleeding, duration of surgery, visual analogue score (VAS), Oswestry Disability Index (ODI), disc height (DH), cross-sectional area of vertebral canal (CSAVC), cross-sectional area of dural sac (CSADS), cross-sectional area of intervertebral foramen (CSAIF), spinal canal volume (SCV), spinal canal volume expansion rate, lumbar lordosis, and sagittal vertical axis were observed and recorded. The efficacy of OLIF combined with pedicle screw internal fixation for lumbar spinal stenosis on spinal canal changes before and after surgery was summarized. RESULTS The results showed that OLIF combined with pedicle screw internal fixation effectively restored disc height and increased the cross-sectional area of the spinal canal. It also had an indirect decompression effect. The intraoperative bleeding and duration of surgery were within acceptable ranges. The VAS and ODI scores significantly improved after surgery, indicating a reduction in pain and improvement in functional disability. The CSAVC, CSADS, CSAIF, SCV, and spinal canal volume expansion rate were all increased postoperatively. Additionally, there was improvement in lumbar lordosis and sagittal vertical axis. We conducted a follow-up of all patients at 1 year after the surgery. The results revealed that the parameter values at 1 year post-surgery showed varying degrees of decrease or increase compared to the immediate postoperative values. However, these values remained statistically significant when compared to the preoperative parameter values (P < 0.05). CONCLUSIONS OLIF combined with pedicle screw internal fixation effectively restores disc height and increases the cross-sectional area of the vertebral canal in patients with LSS, reflecting the indirect decompression effect. Measuring parameters such as DH, CSAVC, CSADS, CSAIF, SCV, and SCV expansion rate before and after surgery provides valuable information for evaluating the efficacy and functional recovery of the lumbar spine in LSS patients treated with OLIF surgery.
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Affiliation(s)
- Wangbing Xu
- Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, Jiangxi 330006, China
| | - Weibing Liu
- Jiangxi University of Chinese Medicine, Nanchang, Jiangxi 330004, China.
| | - Faming Zhong
- Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang, Jiangxi 330006, China
| | - Yu Peng
- The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, Guangdong 518033, China
| | - Xin Liu
- Jiangxi University of Chinese Medicine, Nanchang, Jiangxi 330004, China
| | - Liangkun Yu
- Jiangxi University of Chinese Medicine, Nanchang, Jiangxi 330004, China
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Shahzadi T, Ali MU, Majeed F, Sana MU, Diaz RM, Samad MA, Ashraf I. Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN. Diagnostics (Basel) 2023; 13:2975. [PMID: 37761342 PMCID: PMC10529899 DOI: 10.3390/diagnostics13182975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/14/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient's lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets-multi-ROI and single-ROI-are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach.
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Affiliation(s)
- Turrnum Shahzadi
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (T.S.); (F.M.); (M.U.S.)
| | - Muhammad Usman Ali
- Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan;
| | - Fiaz Majeed
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (T.S.); (F.M.); (M.U.S.)
| | - Muhammad Usman Sana
- Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan; (T.S.); (F.M.); (M.U.S.)
| | - Raquel Martínez Diaz
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain;
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Universidad Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Md Abdus Samad
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Constant C, Aubin CE, Kremers HM, Garcia DVV, Wyles CC, Rouzrokh P, Larson AN. The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications. NORTH AMERICAN SPINE SOCIETY JOURNAL 2023; 15:100236. [PMID: 37599816 PMCID: PMC10432249 DOI: 10.1016/j.xnsj.2023.100236] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 06/14/2023] [Indexed: 08/22/2023]
Abstract
Background Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction. The purpose of this study was to evaluate the current literature and clinical utilization of DL in spine imaging. Methods This study is a scoping review and utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2012 to 2021. A search in PubMed, Web of Science, Embased, and IEEE Xplore databases with syntax specific for DL and medical imaging in spine care applications was conducted to collect all original publications on the subject. Specific data was extracted from the available literature, including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. Results A total of 365 studies (total sample of 232,394 patients) were included and grouped into 4 general applications: diagnostic tools, clinical decision support tools, automated clinical/instrumentation assessment, and clinical outcome prediction. Notable disparities exist in the selected algorithms and the training across multiple disparate databases. The most frequently used algorithms were U-Net and ResNet. A DL model was developed and validated in 92% of included studies, while a pre-existing DL model was investigated in 8%. Of all developed models, only 15% of them have been externally validated. Conclusions Based on this scoping review, DL in spine imaging is used in a broad range of clinical applications, particularly for diagnosing spinal conditions. There is a wide variety of DL algorithms, database characteristics, and training methods. Future studies should focus on external validation of existing models before bringing them into clinical use.
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Affiliation(s)
- Caroline Constant
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
- AO Research Institute Davos, Clavadelerstrasse 8, CH 7270, Davos, Switzerland
| | - Carl-Eric Aubin
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
| | - Hilal Maradit Kremers
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Diana V. Vera Garcia
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Cody C. Wyles
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Pouria Rouzrokh
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Radiology Informatics Laboratory, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Annalise Noelle Larson
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
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Chen M, Kong C, Lin G, Chen W, Guo X, Chen Y, Cheng X, Chen M, Shi C, Xu M, Sun J, Lu C, Ji J. Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study. EClinicalMedicine 2023; 63:102176. [PMID: 37662514 PMCID: PMC10474371 DOI: 10.1016/j.eclinm.2023.102176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/05/2023] Open
Abstract
Background For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.
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Affiliation(s)
- Mingzhen Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Chunli Kong
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Guihan Lin
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Weiyue Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Xinyu Guo
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Yaning Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
| | - Xue Cheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Changsheng Shi
- Department of Interventional Radiology, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, Zhejiang, China
| | - Min Xu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Junhui Sun
- Division of Hepatobiliary and Pancreatic Surgery, Hepatobiliary and Pancreatic Interventional Treatment Centre, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenying Lu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Lishui Hospital, International Institutes of Medicine, School of Medicine, Zhejiaing University, Lishui, Zhejiang 323000, China
- Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China
- Clinical College of the Affiliated Central Hospital, School of Medcine, Lishui University, Lishui 323000, China
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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Altun S, Alkan A, Altun İ. LSS-VGG16: Diagnosis of Lumbar Spinal Stenosis With Deep Learning. Clin Spine Surg 2023; 36:E180-E190. [PMID: 36727890 DOI: 10.1097/bsd.0000000000001418] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/13/2022] [Indexed: 02/03/2023]
Abstract
STUDY DESIGN This was a retrospective study. OBJECTION Lumbar Spinal Stenosis (LSS) is a disease that causes chronic low back pain and can often be confused with herniated disk. In this study, a deep learning-based classification model is proposed to make LSS diagnosis quickly and automatically with an objective tool. SUMMARY OF BACKGROUND DATA LSS is a disease that causes negative consequences such as low back pain, foot numbness, and pain. Diagnosis of this disease is difficult because it is confused with herniated disk and requires serious expertise. The shape and amount of this stenosis are very important in deciding the surgery and the surgical technique to be applied in these patients. When the spinal canal narrows, as a result of compression on these nerves and/or pressure on the vessels feeding the nerves, poor nutrition of the nerves causes loss of function and structure. Image processing techniques are applied in biomedical images such as MR and CT and high classification success is achieved. In this way, computer-aided diagnosis systems can be realized to help the specialist in the diagnosis of different diseases. METHODS To demonstrate the success of the proposed model, different deep learning methods and traditional machine learning techniques have been studied. RESULTS The highest classification success was obtained in the VGG16 method, with 87.70%. CONCLUSIONS The proposed LSS-VGG16 model reveals that a computer-aided diagnosis system can be created for the diagnosis of spinal canal stenosis. In addition, it was observed that higher classification success was achieved compared with similar studies in the literature. This shows that the proposed LSS-VGG16 model will be an important resource for scientists who will work in this field.
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Affiliation(s)
- Sinan Altun
- Department of Electrical and Electronics Engineering
| | - Ahmet Alkan
- Department of Electrical and Electronics Engineering
| | - İdiris Altun
- Department of Neurosurgery, Kahramanmaras Sutcu Imam Universirty, Kahramanmaras, Turkey
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Bharadwaj UU, Christine M, Li S, Chou D, Pedoia V, Link TM, Chin CT, Majumdar S. Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI. Eur Radiol 2023; 33:3435-3443. [PMID: 36920520 PMCID: PMC10566647 DOI: 10.1007/s00330-023-09483-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 12/09/2022] [Accepted: 01/30/2023] [Indexed: 03/16/2023]
Abstract
OBJECTIVES To evaluate a deep learning model for automated and interpretable classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy from lumbar spine MRI. METHODS T2-weighted axial MRI studies of the lumbar spine acquired between 2008 and 2019 were retrospectively selected (n = 200) and graded for central canal stenosis, neural foraminal stenosis, and facet arthropathy. Studies were partitioned into patient-level train (n = 150), validation (n = 20), and test (n = 30) splits. V-Net models were first trained to segment the dural sac and the intervertebral disk, and localize facet and foramen using geometric rules. Subsequently, Big Transfer (BiT) models were trained for downstream classification tasks. An interpretable model for central canal stenosis was also trained using a decision tree classifier. Evaluation metrics included linearly weighted Cohen's kappa score for multi-grade classification and area under the receiver operator characteristic curve (AUROC) for binarized classification. RESULTS Segmentation of the dural sac and intervertebral disk achieved Dice scores of 0.93 and 0.94. Localization of foramen and facet achieved intersection over union of 0.72 and 0.83. Multi-class grading of central canal stenosis achieved a kappa score of 0.54. The interpretable decision tree classifier had a kappa score of 0.80. Pairwise agreement between readers (R1, R2), (R1, R3), and (R2, R3) was 0.86, 0.80, and 0.74. Binary classification of neural foraminal stenosis and facet arthropathy achieved AUROCs of 0.92 and 0.93. CONCLUSION Deep learning systems can be performant as well as interpretable for automated evaluation of lumbar spine MRI including classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy. KEY POINTS • Interpretable deep-learning systems can be developed for the evaluation of clinical lumbar spine MRI. Multi-grade classification of central canal stenosis with a kappa of 0.80 was comparable to inter-reader agreement scores (0.74, 0.80, 0.86). Binary classification of neural foraminal stenosis and facet arthropathy achieved favorable and accurate AUROCs of 0.92 and 0.93, respectively. • While existing deep-learning systems are opaque, leading to clinical deployment challenges, the proposed system is accurate as well as interpretable, providing valuable information to a radiologist in clinical practice.
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Affiliation(s)
- Upasana Upadhyay Bharadwaj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA.
| | - Miranda Christine
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA
| | - Steven Li
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA
| | - Dean Chou
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA
| | - Cynthia T Chin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 185 Berry Street, Suite 350, San Francisco, CA, 94107, USA
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Chen K, Zhai X, Wang S, Li X, Lu Z, Xia D, Li M. Emerging trends and research foci of deep learning in spine: bibliometric and visualization study. Neurosurg Rev 2023; 46:81. [PMID: 37000304 DOI: 10.1007/s10143-023-01987-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/10/2023] [Accepted: 03/26/2023] [Indexed: 04/01/2023]
Abstract
As the cognition of spine develops, deep learning (DL) emerges as a powerful tool with tremendous potential for advancing research in this field. To provide a comprehensive overview of DL-spine research, our study utilized bibliometric and visual methods to retrieve relevant articles from the Web of Science database. VOSviewer and CiteSpace were primarily used for literature measurement and knowledge graph analysis. A total of 273 studies focusing on deep learning in the spine, with a combined total of 2302 citations, were retrieved. Additionally, the overall number of articles published on this topic demonstrated a continuous upward trend. China was the country with the highest number of publications, whereas the USA had the most citations. The two most prominent journals were "European Spine Journal" and "Medical Image Analysis," and the most involved research area was Radiology Nuclear Medicine Medical Imaging. VOSviewer identified three visually distinct clusters: "segmentation," "area," and "neural network." Meanwhile, CiteSpace highlighted "magnetic resonance image" and "lumbar" as the keywords with the longest usage, and "agreement" and "automated detection" as the most commonly used keywords. Although the application of DL in spine is still in its infancy, its future is promising. Intercontinental cooperation, extensive application, and more interpretable algorithms will invigorate DL in the field of spine.
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Affiliation(s)
- Kai Chen
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Xiao Zhai
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Sheng Wang
- Department of Emergency, Shanghai Changhai Hospital, Shanghai, China
| | - Xiaoyu Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Zhikai Lu
- Department of Orthopedics, No. 906 Hospital of Joint Logistic Support Force of PLA, Ningbo, Zhejiang, China.
| | - Demeng Xia
- Luodian Clinical Drug Research Center, Shanghai Baoshan Luodian Hospital, Shanghai University, Shanghai, China.
- Emergency Department, Naval Hospital of Eastern Theater, Zhoushan, Zhejiang, China.
| | - Ming Li
- Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
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Hallinan JTPD, Zhu L, Zhang W, Ge S, Muhamat Nor FE, Ong HY, Eide SE, Cheng AJL, Kuah T, Lim DSW, Low XZ, Yeong KY, AlMuhaish MI, Alsooreti A, Kumarakulasinghe NB, Teo EC, Yap QV, Chan YH, Lin S, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A. Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT. Front Oncol 2023; 13:1151073. [PMID: 37213273 PMCID: PMC10193838 DOI: 10.3389/fonc.2023.1151073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/16/2023] [Indexed: 05/23/2023] Open
Abstract
Introduction Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment. Methods Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated. Results Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001). Conclusion Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- *Correspondence: James Thomas Patrick Decourcy Hallinan,
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Faimee Erwan Muhamat Nor
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Han Yang Ong
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Sterling Ellis Eide
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Amanda J. L. Cheng
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Kuan Yuen Yeong
- Department of Radiology, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Mona I. AlMuhaish
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Radiology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ahmed Mohamed Alsooreti
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Imaging, Salmaniya Medical Complex, Manama, Bahrain
| | | | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Shuxun Lin
- Division of Spine Surgery, Department of Orthopaedic Surgery, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Bousson V, Benoist N, Guetat P, Attané G, Salvat C, Perronne L. Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going? Joint Bone Spine 2023; 90:105493. [PMID: 36423783 DOI: 10.1016/j.jbspin.2022.105493] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 11/23/2022]
Abstract
The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images. The various musculoskeletal applications of deep learning are the detection of abnormalities on X-rays or cross-sectional images (CT, MRI), for example the detection of fractures, meniscal tears, anterior cruciate ligament tears, degenerative lesions of the spine, bone metastases, classification of e.g., dural sac stenosis, degeneration of intervertebral discs, assessment of skeletal age, and segmentation, for example of cartilage. Software developments are already impacting the daily practice of orthopedic imaging by automatically detecting fractures on radiographs. Improving image acquisition protocols, improving the quality of low-dose CT images, reducing acquisition times in MRI, or improving MR image resolution is possible through deep learning. Deep learning offers an automated way to offload time-consuming manual processes and improve practitioner performance. This article reviews the current state of AI in musculoskeletal imaging.
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Affiliation(s)
- Valérie Bousson
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France.
| | - Nicolas Benoist
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France
| | - Pierre Guetat
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France
| | - Grégoire Attané
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France
| | - Cécile Salvat
- Department of Medical Physics, hôpital Lariboisière, AP-HP Nord-université Paris Cité, Paris, France
| | - Laetitia Perronne
- Service de radiologie ostéoarticulaire, hôpital Lariboisière, AP-HP Nord-université Paris Cité, 75010 Paris, France; Laboratoire B3OA, CNRS UMR 7052, Paris, France
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Pang C, Su Z, Lin L, Lin G, He J, Lu H, Feng Q, Pang S. Automated measurement of spine indices on axial MR images for lumbar spinal stenosis diagnosis using segmentation-guided regression network. Med Phys 2023; 50:104-116. [PMID: 36029008 DOI: 10.1002/mp.15961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/03/2022] [Accepted: 08/21/2022] [Indexed: 01/25/2023] Open
Abstract
PURPOSE Automated measurement of spine indices on axial magnetic resonance (MR) images plays a significant role in lumbar spinal stenosis diagnosis. Existing direct spine indices measurement approaches fail to explicitly focus on the task-specific region or feature channel with the additional information for guiding. We aim to achieve accurate spine indices measurement by introducing the guidance of the segmentation task. METHODS In this paper, we propose a segmentation-guided regression network (SGRNet) to achieve automated spine indices measurement. SGRNet consists of a segmentation path for generating the spine segmentation prediction and a regression path for producing spine indices estimation. The segmentation path is a U-Net-like network which includes a segmentation encoder and a decoder which generates multilevel segmentation features and segmentation prediction. The proposed segmentation-guided attention module (SGAM) in the regression encoder extracts the attention-aware regression feature under the guidance of the segmentation feature. Based on the attention-aware regression feature, a fully connected layer is utilized to output the accurate spine indices estimation. RESULTS Experiments on the open-access Lumbar Spine MRI data set show that SGRNet achieves state-of-the-art performance with a mean absolute error of 0.49 mm and mean Pearson correlation coefficient of 0.956 for four indices estimation. CONCLUSIONS The proposed SGAM in SGRNet is capable of improving the performance of spine indices measurement by focusing on the task-specific region and feature channel under the guidance of the segmentation task.
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Affiliation(s)
- Chunlan Pang
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhihai Su
- Department of Spinal Surgery, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Liyan Lin
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Guoye Lin
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Ji He
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
| | - Hai Lu
- Department of Spinal Surgery, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Shumao Pang
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China
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Zheng Y, Bai C, Zhang K, Han Q, Guan Q, Liu Y, Zheng Z, Xia Y, Zhu P. Deep-learning based quantification model for hip bone marrow edema and synovitis in patients with spondyloarthritis based on magnetic resonance images. Front Physiol 2023; 14:1132214. [PMID: 36935744 PMCID: PMC10020192 DOI: 10.3389/fphys.2023.1132214] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 02/20/2023] [Indexed: 03/06/2023] Open
Abstract
Objectives: Hip inflammation is one of the most common complications in patients with spondyloarthritis (SpA). Herein, we employed use of a deep learning-based magnetic resonance imaging (MRI) evaluation model to identify irregular and multiple inflammatory lesions of the hip. Methods: All of the SpA patients were enrolled at the Xijing Hospital. The erythrocyte sediment rate (ESR), C-reactive protein (CRP), hip function Harris score, and disease activity were evaluated by clinicians. Manual MRI annotations including bone marrow edema (BME) and effusion/synovitis, and a hip MRI scoring system (HIMRISS) assessment was performed by experienced musculoskeletal radiologists. The segmentation accuracies of four deep learning models, including U-Net, UNet++, Attention-Unet, and HRNet, were compared using five-fold cross-validation. The clinical agreement of U-Net was evaluated with clinical symptoms and HIMRISS results. Results: A total of 1945 MRI slices of STIR/T2WI sequences were obtained from 195 SpA patients with hip involvement. After the five-fold cross-validation, U-Net achieved an average segmentation accuracy of 88.48% for the femoral head and 69.36% for inflammatory lesions, which are higher than those obtained by the other three models. The UNet-score, which was calculated based on the same MRI slices as HIMRISS, was significantly correlated with the HIMRISS scores and disease activity indexes (p values <0.05). Conclusion: This deep-learning based automatic MRI evaluation model could achieve similar quantification performance as an expert radiologist, and it has the potential to improve the accuracy and efficiency of clinical diagnosis for SpA patients with hip involvement.
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Affiliation(s)
- Yan Zheng
- Department of Clinical Immunology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- National Translational Science Center for Molecular Medicine, Xi’an, China
| | - Chao Bai
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Kui Zhang
- Department of Clinical Immunology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- National Translational Science Center for Molecular Medicine, Xi’an, China
| | - Qing Han
- Department of Clinical Immunology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- National Translational Science Center for Molecular Medicine, Xi’an, China
| | - Qingbiao Guan
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Ying Liu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Zhaohui Zheng
- Department of Clinical Immunology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- National Translational Science Center for Molecular Medicine, Xi’an, China
| | - Yong Xia
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Ping Zhu
- Department of Clinical Immunology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- National Translational Science Center for Molecular Medicine, Xi’an, China
- *Correspondence: Ping Zhu,
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Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches. Invest Radiol 2023; 58:3-13. [PMID: 36070548 DOI: 10.1097/rli.0000000000000907] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.
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Park S, Kim JK, Chang MC, Park JJ, Yang JJ, Lee GW. Assessment of Fusion After Anterior Cervical Discectomy and Fusion Using Convolutional Neural Network Algorithm. Spine (Phila Pa 1976) 2022; 47:1645-1650. [PMID: 35905310 DOI: 10.1097/brs.0000000000004439] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/28/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND A convolutional neural network (CNN) is a deep learning (DL) model specialized for image processing, analysis, and classification. OBJECTIVE In this study, we evaluated whether a CNN model using lateral cervical spine radiographs as input data can help assess fusion after anterior cervical discectomy and fusion (ACDF). STUDY DESIGN Diagnostic imaging study using DL. PATIENT SAMPLE We included 187 patients who underwent ACDF and fusion assessment with postoperative one-year computed tomography and neutral and dynamic lateral cervical spine radiographs. OUTCOME MEASURES The performance of the CNN-based DL algorithm was evaluated in terms of accuracy and area under the curve. MATERIALS AND METHODS Fusion or nonunion was confirmed by cervical spine computed tomography. Among the 187 patients, 69.5% (130 patients) were randomly selected as the training set, and the remaining 30.5% (57 patients) were assigned to the validation set to evaluate model performance. Radiographs of the cervical spine were used as input images to develop a CNN-based DL algorithm. The CNN algorithm used three radiographs (neutral, flexion, and extension) per patient and showed the diagnostic results as fusion (0) or nonunion (1) for each radiograph. By combining the results of the three radiographs, the final decision for a patient was determined to be fusion (fusion ≥2) or nonunion (fusion ≤1). By combining the results of the three radiographs, the final decision for a patient was determined as fusion (fusion ≥2) or nonunion (nonunion ≤1). RESULTS The CNN-based DL model demonstrated an accuracy of 89.5% and an area under the curve of 0.889 (95% confidence interval, 0.793-0.984). CONCLUSION The CNN algorithm for fusion assessment after ACDF trained using lateral cervical radiographs showed a relatively high diagnostic accuracy of 89.5% and is expected to be a useful aid in detecting pseudarthrosis.
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Affiliation(s)
- Sehan Park
- Department of Orthopedic Surgery, Dongguk University Ilsan Hospital, Goyang-si, Gyeonggi-do Province, Republic of Korea
| | - Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Gyeonggi-do Province, Republic of Korea
| | - Min Cheol Chang
- Department of Physical Medicine and Rehabilitation, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, Gyeongsang Province, Republic of Korea
| | - Jeong Jin Park
- Department of Orthopedic Surgery, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, Gyeongsang Province, Republic of Korea
| | - Jae Jun Yang
- Department of Orthopedic Surgery, Dongguk University Ilsan Hospital, Goyang-si, Gyeonggi-do Province, Republic of Korea
| | - Gun Woo Lee
- Department of Physical Medicine and Rehabilitation, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, Gyeongsang Province, Republic of Korea
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Clinical Application of Large Channel Endoscopic Systems with Full Endoscopic Visualization Technique in Lumbar Central Spinal Stenosis: A Retrospective Cohort Study. Pain Ther 2022; 11:1309-1326. [PMID: 36057015 PMCID: PMC9633890 DOI: 10.1007/s40122-022-00428-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/15/2022] [Indexed: 10/14/2022] Open
Abstract
INTRODUCTION Recently, large channel endoscopic systems and full endoscopic visualization technique have been used to perform unilateral laminotomy for bilateral decompression (ULBD) treatment for lumbar central spinal stenosis (LCSS). However, various endoscopic systems possess different design parameters, which may affect the technical points and treatment outcomes. The object of this retrospective study was to compare the efficiency, safety, and effectiveness of ULBD under the iLESSYS Delta system versus the Endo-Surgi Plus system. METHODS In the period from October 2020 to April 2021, ULBD was performed using the iLESSYS Delta system or Endo-Surgi Plus system to treat LCSS. Patients were classified into two groups based on the endoscopy system employed. Patient demographics, perioperative indexes, complications, and imaging characteristics were reviewed. Clinical outcomes were quantified using back and leg visual analog scale (VAS) scores and Oswestry Disability Index (ODI) at the time points of follow-up. RESULTS Thirty-two patients were assigned to the iLESSYS Delta system group and 37 to the Endo-Surgi Plus system group. In the comparison between the two groups, the Endo-Surgi Plus system possessed a shorter incision length and operation time (p < 0.005), and no statistical differences in other aspects were observed. The dural sacs of both groups were significantly expanded postoperatively compared to preoperatively (p < 0.001). Both groups experienced improvements in VAS and ODI scores at all time points (p < 0.001) and equally low frequency of complications. CONCLUSIONS Current research suggests that both the Endo-Surgi Plus system and iLESSYS Delta system achieved favorable high safety and clinical outcomes in ULBD for treatment of LCSS. The use of a fully visualized trephine may have increased the efficiency of the Endo-Surgi Plus system. Moreover, the Endo-Surgi Plus system may be associated with a wider decompression range and indications.
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Zhao J, Sun L, Zhou X, Huang S, Si H, Zhang D. Residual-atrous attention network for lumbosacral plexus segmentation with MR image. Comput Med Imaging Graph 2022; 100:102109. [DOI: 10.1016/j.compmedimag.2022.102109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/12/2022] [Accepted: 07/28/2022] [Indexed: 10/15/2022]
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A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features. SENSORS 2022; 22:s22145205. [PMID: 35890885 PMCID: PMC9318445 DOI: 10.3390/s22145205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/19/2022] [Accepted: 06/24/2022] [Indexed: 02/01/2023]
Abstract
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.
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Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT. Cancers (Basel) 2022; 14:cancers14133219. [PMID: 35804990 PMCID: PMC9264856 DOI: 10.3390/cancers14133219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 02/02/2023] Open
Abstract
Background: Metastatic epidural spinal cord compression (MESCC) is a disastrous complication of advanced malignancy. Deep learning (DL) models for automatic MESCC classification on staging CT were developed to aid earlier diagnosis. Methods: This retrospective study included 444 CT staging studies from 185 patients with suspected MESCC who underwent MRI spine studies within 60 days of the CT studies. The DL model training/validation dataset consisted of 316/358 (88%) and the test set of 42/358 (12%) CT studies. Training/validation and test datasets were labeled in consensus by two subspecialized radiologists (6 and 11-years-experience) using the MRI studies as the reference standard. Test sets were labeled by the developed DL models and four radiologists (2−7 years of experience) for comparison. Results: DL models showed almost-perfect interobserver agreement for classification of CT spine images into normal, low, and high-grade MESCC, with kappas ranging from 0.873−0.911 (p < 0.001). The DL models (lowest κ = 0.873, 95% CI 0.858−0.887) also showed superior interobserver agreement compared to two of the four radiologists for three-class classification, including a specialist (κ = 0.820, 95% CI 0.803−0.837) and general radiologist (κ = 0.726, 95% CI 0.706−0.747), both p < 0.001. Conclusion: DL models for the MESCC classification on a CT showed comparable to superior interobserver agreement to radiologists and could be used to aid earlier diagnosis.
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Lim DSW, Makmur A, Zhu L, Zhang W, Cheng AJL, Sia DSY, Eide SE, Ong HY, Jagmohan P, Tan WC, Khoo VM, Wong YM, Thian YL, Baskar S, Teo EC, Algazwi DAR, Yap QV, Chan YH, Tan JH, Kumar N, Ooi BC, Yoshioka H, Quek ST, Hallinan JTPD. Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI. Radiology 2022; 305:160-166. [PMID: 35699577 DOI: 10.1148/radiol.220076] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Lumbar spine MRI studies are widely used for back pain assessment. Interpretation involves grading lumbar spinal stenosis, which is repetitive and time consuming. Deep learning (DL) could provide faster and more consistent interpretation. Purpose To assess the speed and interobserver agreement of radiologists for reporting lumbar spinal stenosis with and without DL assistance. Materials and Methods In this retrospective study, a DL model designed to assist radiologists in the interpretation of spinal canal, lateral recess, and neural foraminal stenoses on lumbar spine MRI scans was used. Randomly selected lumbar spine MRI studies obtained in patients with back pain who were 18 years and older over a 3-year period, from September 2015 to September 2018, were included in an internal test data set. Studies with instrumentation and scoliosis were excluded. Eight radiologists, each with 2-13 years of experience in spine MRI interpretation, reviewed studies with and without DL model assistance with a 1-month washout period. Time to diagnosis (in seconds) and interobserver agreement (using Gwet κ) were assessed for stenosis grading for each radiologist with and without the DL model and compared with test data set labels provided by an external musculoskeletal radiologist (with 32 years of experience) as the reference standard. Results Overall, 444 images in 25 patients (mean age, 51 years ± 20 [SD]; 14 women) were evaluated in a test data set. DL-assisted radiologists had a reduced interpretation time per spine MRI study, from a mean of 124-274 seconds (SD, 25-88 seconds) to 47-71 seconds (SD, 24-29 seconds) (P < .001). DL-assisted radiologists had either superior or equivalent interobserver agreement for all stenosis gradings compared with unassisted radiologists. DL-assisted general and in-training radiologists improved their interobserver agreement for four-class neural foraminal stenosis, with κ values of 0.71 and 0.70 (with DL) versus 0.39 and 0.39 (without DL), respectively (both P < .001). Conclusion Radiologists who were assisted by deep learning for interpretation of lumbar spinal stenosis on MRI scans showed a marked reduction in reporting time and superior or equivalent interobserver agreement for all stenosis gradings compared with radiologists who were unassisted by deep learning. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hayashi in this issue.
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Affiliation(s)
- Desmond Shi Wei Lim
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Andrew Makmur
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Lei Zhu
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Wenqiao Zhang
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Amanda J L Cheng
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - David Soon Yiew Sia
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Sterling Ellis Eide
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Han Yang Ong
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Pooja Jagmohan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Wei Chuan Tan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Vanessa Meihui Khoo
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Ying Mei Wong
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yee Liang Thian
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Sangeetha Baskar
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Ee Chin Teo
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Diyaa Abdul Rauf Algazwi
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Qai Ven Yap
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Yiong Huak Chan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Jiong Hao Tan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Naresh Kumar
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Beng Chin Ooi
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Hiroshi Yoshioka
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - Swee Tian Quek
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
| | - James Thomas Patrick Decourcy Hallinan
- From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.)
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Hayashi D. Multitask Deep Learning Tools Are Needed for Clinical Practice-Especially for Low Back Pain. Radiology 2022; 305:167-168. [PMID: 35699583 DOI: 10.1148/radiol.221202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Daichi Hayashi
- From the Department of Radiology, Stony Brook University Renaissance School of Medicine, State University of New York, 101 Nicolls Rd, HSc Level 4, Room 120, Stony Brook, NY 11794-8460
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Gao KT, Tibrewala R, Hess M, Bharadwaj UU, Inamdar G, Link TM, Chin CT, Pedoia V, Majumdar S. Automatic detection and voxel‐wise mapping of lumbar spine Modic changes with deep learning. JOR Spine 2022; 5:e1204. [PMID: 35783915 PMCID: PMC9238279 DOI: 10.1002/jsp2.1204] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/01/2022] [Accepted: 05/05/2022] [Indexed: 11/14/2022] Open
Abstract
Background Modic changes (MCs) are the most prevalent classification system for describing magnetic resonance imaging (MRI) signal intensity changes in the vertebrae. However, there is a growing need for novel quantitative and standardized methods of characterizing these anomalies, particularly for lesions of transitional or mixed nature, due to the lack of conclusive evidence of their associations with low back pain. This retrospective imaging study aims to develop an interpretable deep learning‐based detection tool for voxel‐wise mapping of MCs. Methods Seventy‐five lumbar spine MRI exams that presented with acute‐to‐chronic low back pain, radiculopathy, and other symptoms of the lumbar spine were enrolled. The pipeline consists of two deep convolutional neural networks to generate an interpretable voxel‐wise Modic map. First, an autoencoder was trained to segment vertebral bodies from T1‐weighted sagittal lumbar spine images. Next, two radiologists segmented and labeled MCs from a combined T1‐ and T2‐weighted assessment to serve as ground truth for training a second autoencoder that performs segmentation of MCs. The voxels in the detected regions were then categorized to the appropriate Modic type using a rule‐based signal intensity algorithm. Post hoc, three radiologists independently graded a second dataset with the aid of the model predictions in an artificial (AI)‐assisted experiment. Results The model successfully identified the presence of changes in 85.7% of samples in the unseen test set with a sensitivity of 0.71 (±0.072), specificity of 0.95 (±0.022), and Cohen's kappa score of 0.63. In the AI‐assisted experiment, the agreement between the junior radiologist and the senior neuroradiologist significantly improved from Cohen's kappa score of 0.52 to 0.58 (p < 0.05). Conclusions This deep learning‐based approach demonstrates substantial agreement with radiologists and may serve as a tool to improve inter‐rater reliability in the assessment of MCs.
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Affiliation(s)
- Kenneth T. Gao
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
- Department of Bioengineering University of California Berkeley–University of California San Francisco Graduate Program in Bioengineering Berkeley California USA
| | - Radhika Tibrewala
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
| | - Madeline Hess
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
| | - Upasana U. Bharadwaj
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
| | - Gaurav Inamdar
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
| | - Thomas M. Link
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
| | - Cynthia T. Chin
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA
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Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports. J Digit Imaging 2022; 35:524-533. [PMID: 35149938 PMCID: PMC9156601 DOI: 10.1007/s10278-022-00595-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 12/15/2022] Open
Abstract
Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.
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Hallinan JTPD, Zhu L, Zhang W, Lim DSW, Baskar S, Low XZ, Yeong KY, Teo EC, Kumarakulasinghe NB, Yap QV, Chan YH, Lin S, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A. Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI. Front Oncol 2022; 12:849447. [PMID: 35600347 PMCID: PMC9114468 DOI: 10.3389/fonc.2022.849447] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/18/2022] [Indexed: 11/13/2022] Open
Abstract
Background Metastatic epidural spinal cord compression (MESCC) is a devastating complication of advanced cancer. A deep learning (DL) model for automated MESCC classification on MRI could aid earlier diagnosis and referral. Purpose To develop a DL model for automated classification of MESCC on MRI. Materials and Methods Patients with known MESCC diagnosed on MRI between September 2007 and September 2017 were eligible. MRI studies with instrumentation, suboptimal image quality, and non-thoracic regions were excluded. Axial T2-weighted images were utilized. The internal dataset split was 82% and 18% for training/validation and test sets, respectively. External testing was also performed. Internal training/validation data were labeled using the Bilsky MESCC classification by a musculoskeletal radiologist (10-year experience) and a neuroradiologist (5-year experience). These labels were used to train a DL model utilizing a prototypical convolutional neural network. Internal and external test sets were labeled by the musculoskeletal radiologist as the reference standard. For assessment of DL model performance and interobserver variability, test sets were labeled independently by the neuroradiologist (5-year experience), a spine surgeon (5-year experience), and a radiation oncologist (11-year experience). Inter-rater agreement (Gwet’s kappa) and sensitivity/specificity were calculated. Results Overall, 215 MRI spine studies were analyzed [164 patients, mean age = 62 ± 12(SD)] with 177 (82%) for training/validation and 38 (18%) for internal testing. For internal testing, the DL model and specialists all showed almost perfect agreement (kappas = 0.92–0.98, p < 0.001) for dichotomous Bilsky classification (low versus high grade) compared to the reference standard. Similar performance was seen for external testing on a set of 32 MRI spines with the DL model and specialists all showing almost perfect agreement (kappas = 0.94–0.95, p < 0.001) compared to the reference standard. Conclusion A DL model showed comparable agreement to a subspecialist radiologist and clinical specialists for the classification of malignant epidural spinal cord compression and could optimize earlier diagnosis and surgical referral.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Lei Zhu
- NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Wenqiao Zhang
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Desmond Shi Wei Lim
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Sangeetha Baskar
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kuan Yuen Yeong
- Department of Radiology, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | | | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore
| | - Shuxun Lin
- Division of Spine Surgery, Department of Orthopaedic Surgery, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.,Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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D’Antoni F, Russo F, Ambrosio L, Bacco L, Vollero L, Vadalà G, Merone M, Papalia R, Denaro V. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105971. [PMID: 35627508 PMCID: PMC9141006 DOI: 10.3390/ijerph19105971] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/09/2022] [Accepted: 05/12/2022] [Indexed: 12/10/2022]
Abstract
Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.
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Affiliation(s)
- Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
| | - Fabrizio Russo
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
- Correspondence: (F.R.); (M.M.)
| | - Luca Ambrosio
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Luca Bacco
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
- ItaliaNLP Lab, Istituto di Linguistica Computazionale “Antonio Zampolli”, National Research Council, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy
- Webmonks S.r.l., Via del Triopio, 5, 00178 Rome, Italy
| | - Luca Vollero
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
| | - Gianluca Vadalà
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
- Correspondence: (F.R.); (M.M.)
| | - Rocco Papalia
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Vincenzo Denaro
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
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Deer TR, Grider JS, Pope JE, Lamer TJ, Wahezi SE, Hagedorn JM, Falowski S, Tolba R, Shah JM, Strand N, Escobar A, Malinowski M, Bux A, Jassal N, Hah J, Weisbein J, Tomycz ND, Jameson J, Petersen EA, Sayed D. Best Practices for Minimally Invasive Lumbar Spinal Stenosis Treatment 2.0 (MIST): Consensus Guidance from the American Society of Pain and Neuroscience (ASPN). J Pain Res 2022; 15:1325-1354. [PMID: 35546905 PMCID: PMC9084394 DOI: 10.2147/jpr.s355285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/06/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction Methods Results Discussion Conclusion
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Affiliation(s)
- Timothy R Deer
- Centers for Pain Relief, Charleston, WV, USA
- Correspondence: Timothy R Deer, The Spine and Nerve Centers of the Virginias, 400 Court Street, Suite 100, Charleston, WV, 25301, USA, Tel +1 304 347-6141, Email
| | - Jay S Grider
- UK HealthCare Pain Services, Department of Anesthesiology, University of Kentucky College of Medicine, Lexington, KY, USA
| | | | - Tim J Lamer
- Division of Pain Medicine, Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA
| | - Sayed E Wahezi
- Montefiore Medical Center, SUNY-Buffalo, Buffalo, NY, USA
| | - Jonathan M Hagedorn
- Department of Anesthesiology and Perioperative Medicine, Division of Pain Medicine, Mayo Clinic, Rochester, MN, USA
| | - Steven Falowski
- Director Functional Neurosurgery, Neurosurgical Associates of Lancaster, Lancaster, PA, USA
| | - Reda Tolba
- Pain Management Department, Anesthesiology Institute, Cleveland Clinic, Abu Dhabi, UAE
| | - Jay M Shah
- SamWell Institute for Pain Management, Colonia, NJ, USA
| | - Natalie Strand
- Department of Anesthesiology, Division of Pain Medicine, Mayo Clinic, Phoenix, AZ, USA
| | - Alex Escobar
- Department of Anesthesiology and Pain Medicine, University of Toledo Medical Center, Toledo, OH, USA
| | | | - Anjum Bux
- Bux Pain Management, Lexington, KY, USA
| | | | - Jennifer Hah
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Palo Alto, CA, USA
| | | | - Nestor D Tomycz
- Department of Neurological Surgery, Allegheny General Hospital, Allegheny Health Network, Pittsburgh, PA, USA
| | | | - Erika A Petersen
- Department of Neurosurgery, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Dawood Sayed
- Pain Medicine, Multidisciplinary Pain Fellowship, The University of Kansas Health System, Kansas City, KS, USA
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Diagnostic Performance for Detecting Bone Marrow Edema of the Hip on Dual-Energy CT: Deep Learning Model vs. Musculoskeletal Physicians and Radiologists. Eur J Radiol 2022; 152:110337. [DOI: 10.1016/j.ejrad.2022.110337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/30/2022] [Accepted: 04/21/2022] [Indexed: 02/03/2023]
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Sun S, Tan ET, Mintz DN, Sahr M, Endo Y, Nguyen J, Lebel RM, Carrino JA, Sneag DB. Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI. Eur Radiol 2022; 32:6167-6177. [PMID: 35322280 DOI: 10.1007/s00330-022-08708-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/24/2022] [Accepted: 03/05/2022] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To compare interobserver agreement and image quality of 3D T2-weighted fast spin echo (T2w-FSE) L-spine MRI images processed with a deep learning reconstruction (DLRecon) against standard-of-care (SOC) reconstruction, as well as against 2D T2w-FSE images. The hypothesis was that DLRecon 3D T2w-FSE would afford improved image quality and similar interobserver agreement compared to both SOC 3D and 2D T2w-FSE. METHODS Under IRB approval, patients who underwent routine 3-T lumbar spine (L-spine) MRI from August 17 to September 17, 2020, with both isotropic 3D and 2D T2w-FSE sequences, were retrospectively included. A DLRecon algorithm, with denoising and sharpening properties was applied to SOC 3D k-space to generate 3D DLRecon images. Four musculoskeletal radiologists blinded to reconstruction status evaluated randomized images for motion artifact, image quality, central/foraminal stenosis, disc degeneration, annular fissure, disc herniation, and presence of facet joint cysts. Inter-rater agreement for each graded variable was evaluated using Conger's kappa (κ). RESULTS Thirty-five patients (mean age 58 ± 19, 26 female) were evaluated. 3D DLRecon demonstrated statistically significant higher median image quality score (2.0/2) when compared to SOC 3D (1.0/2, p < 0.001), 2D axial (1.0/2, p < 0.001), and 2D sagittal sequences (1.0/2, p value < 0.001). κ ranges (and 95% CI) for foraminal stenosis were 0.55-0.76 (0.32-0.86) for 3D DLRecon, 0.56-0.73 (0.35-0.84) for SOC 3D, and 0.58-0.71 (0.33-0.84) for 2D. Mean κ (and 95% CI) for central stenosis at L4-5 were 0.98 (0.96-0.99), 0.97 (0.95-0.99), and 0.98 (0.96-0.99) for 3D DLRecon, 3D SOC and 2D, respectively. CONCLUSIONS DLRecon 3D T2w-FSE L-spine MRI demonstrated higher image quality and similar interobserver agreement for graded variables of interest when compared to 3D SOC and 2D imaging. KEY POINTS • 3D DLRecon T2w-FSE isotropic lumbar spine MRI provides improved image quality when compared to 2D MRI, with similar interobserver agreement for clinical evaluation of pathology. • 3D DLRecon images demonstrated better image quality score (2.0/2) when compared to standard-of-care (SOC) 3D (1.0/2), p value < 0.001; 2D axial (1.0/2), p value < 0.001; and 2D sagittal sequences (1.0/2), p value < 0.001. • Interobserver agreement for major variables of interest was similar among all sequences and reconstruction types. For foraminal stenosis, κ ranged from 0.55 to 0.76 (95% CI 0.32-0.86) for 3D DLRecon, 0.56-0.73 (95% CI 0.35-0.84) for standard-of-care (SOC) 3D, and 0.58-0.71 (95% CI 0.33-0.84) for 2D.
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Affiliation(s)
- Simon Sun
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Ek Tsoon Tan
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Douglas N Mintz
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Meghan Sahr
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Yoshimi Endo
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Joseph Nguyen
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | | | - John A Carrino
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Darryl B Sneag
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
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