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Jang SJ, Alpaugh K, Kunze KN, Li TY, Mayman DJ, Vigdorchik JM, Jerabek SA, Gausden EB, Sculco PK. Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty. J Arthroplasty 2024; 39:1191-1198.e2. [PMID: 38007206 DOI: 10.1016/j.arth.2023.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND The radiographic assessment of bone morphology impacts implant selection and fixation type in total hip arthroplasty (THA) and is important to minimize the risk of periprosthetic femur fracture (PFF). We utilized a deep-learning algorithm to automate femoral radiographic parameters and determined which automated parameters were associated with early PFF. METHODS Radiographs from a publicly available database and from patients undergoing primary cementless THA at a high-volume institution (2016 to 2020) were obtained. A U-Net algorithm was trained to segment femoral landmarks for bone morphology parameter automation. Automated parameters were compared against that of a fellowship-trained surgeon and compared in an independent cohort of 100 patients who underwent THA (50 with early PFF and 50 controls matched by femoral component, age, sex, body mass index, and surgical approach). RESULTS On the independent cohort, the algorithm generated 1,710 unique measurements for 95 images (5% lesser trochanter identification failure) in 22 minutes. Medullary canal width, femoral cortex width, canal flare index, morphological cortical index, canal bone ratio, and canal calcar ratio had good-to-excellent correlation with surgeon measurements (Pearson's correlation coefficient: 0.76 to 0.96). Canal calcar ratios (0.43 ± 0.08 versus 0.40 ± 0.07) and canal bone ratios (0.39 ± 0.06 versus 0.36 ± 0.06) were higher (P < .05) in the PFF cohort when comparing the automated parameters. CONCLUSIONS Deep-learning automated parameters demonstrated differences in patients who had and did not have early PFF after cementless primary THA. This algorithm has the potential to complement and improve patient-specific PFF risk-prediction tools.
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Affiliation(s)
- Seong J Jang
- Weill Cornell College of Medicine, New York, New York; Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York
| | - Kyle Alpaugh
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, Massachusetts
| | - Kyle N Kunze
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York
| | - Tim Y Li
- Weill Cornell College of Medicine, New York, New York
| | - David J Mayman
- Department of Orthopedic Surgery, Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, New York
| | - Jonathan M Vigdorchik
- Department of Orthopedic Surgery, Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, New York
| | - Seth A Jerabek
- Department of Orthopedic Surgery, Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, New York
| | - Elizabeth B Gausden
- Department of Orthopedic Surgery, Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, New York
| | - Peter K Sculco
- Department of Orthopedic Surgery, Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, New York
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Hendrix N, Hendrix W, Maresch B, van Amersfoort J, Oosterveld-Bonsma T, Kolderman S, Vestering M, Zielinski S, Rutten K, Dammeier J, Ong LLS, van Ginneken B, Rutten M. Artificial intelligence for automated detection and measurements of carpal instability signs on conventional radiographs. Eur Radiol 2024:10.1007/s00330-024-10744-1. [PMID: 38634877 DOI: 10.1007/s00330-024-10744-1] [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: 11/03/2023] [Revised: 02/26/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVES To develop and validate an artificial intelligence (AI) system for measuring and detecting signs of carpal instability on conventional radiographs. MATERIALS AND METHODS Two case-control datasets of hand and wrist radiographs were retrospectively acquired at three hospitals (hospitals A, B, and C). Dataset 1 (2178 radiographs from 1993 patients, hospitals A and B, 2018-2019) was used for developing an AI system for measuring scapholunate (SL) joint distances, SL and capitolunate (CL) angles, and carpal arc interruptions. Dataset 2 (481 radiographs from 217 patients, hospital C, 2017-2021) was used for testing, and with a subsample (174 radiographs from 87 patients), an observer study was conducted to compare its performance to five clinicians. Evaluation metrics included mean absolute error (MAE), sensitivity, and specificity. RESULTS Dataset 2 included 258 SL distances, 189 SL angles, 191 CL angles, and 217 carpal arc labels obtained from 217 patients (mean age, 51 years ± 23 [standard deviation]; 133 women). The MAE in measuring SL distances, SL angles, and CL angles was respectively 0.65 mm (95%CI: 0.59, 0.72), 7.9 degrees (95%CI: 7.0, 8.9), and 5.9 degrees (95%CI: 5.2, 6.6). The sensitivity and specificity for detecting arc interruptions were 83% (95%CI: 74, 91) and 64% (95%CI: 56, 71). The measurements were largely comparable to those of the clinicians, while arc interruption detections were more accurate than those of most clinicians. CONCLUSION This study demonstrates that a newly developed automated AI system accurately measures and detects signs of carpal instability on conventional radiographs. CLINICAL RELEVANCE STATEMENT This system has the potential to improve detections of carpal arc interruptions and could be a promising tool for supporting clinicians in detecting carpal instability.
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Affiliation(s)
- Nils Hendrix
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
- Jheronimus Academy of Data Science, Sint Janssingel 92, 5211 DA, 's-Hertogenbosch, The Netherlands.
| | - Ward Hendrix
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Department of Radiology, Jeroen Bosch Ziekenhuis, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
| | - Bas Maresch
- Department of Radiology, Ziekenhuis Gelderse Vallei, Willy Brandtlaan 10, 6717 RP, Ede, The Netherlands
| | - Job van Amersfoort
- Department of Surgery, Ziekenhuis Gelderse Vallei, Willy Brandtlaan 10, 6717 RP, Ede, The Netherlands
| | - Tineke Oosterveld-Bonsma
- Department of Radiology, Ziekenhuis Gelderse Vallei, Willy Brandtlaan 10, 6717 RP, Ede, The Netherlands
| | - Stephanie Kolderman
- Department of Radiology, Ziekenhuis Gelderse Vallei, Willy Brandtlaan 10, 6717 RP, Ede, The Netherlands
| | - Myrthe Vestering
- Department of Radiology, Ziekenhuis Gelderse Vallei, Willy Brandtlaan 10, 6717 RP, Ede, The Netherlands
| | - Stephanie Zielinski
- Department of Surgery, Ziekenhuis Gelderse Vallei, Willy Brandtlaan 10, 6717 RP, Ede, The Netherlands
| | - Karlijn Rutten
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Jan Dammeier
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Lee-Ling Sharon Ong
- Jheronimus Academy of Data Science, Sint Janssingel 92, 5211 DA, 's-Hertogenbosch, The Netherlands
- Cognitive Science and Artificial Intelligence Department, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, The Netherlands
| | - Bram van Ginneken
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Matthieu Rutten
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Department of Radiology, Jeroen Bosch Ziekenhuis, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
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Burkow J, Holste G, Otjen J, Perez F, Junewick J, Zbojniewicz A, Romberg E, Menashe S, Frost J, Alessio A. High sensitivity methods for automated rib fracture detection in pediatric radiographs. Sci Rep 2024; 14:8372. [PMID: 38600311 PMCID: PMC11006902 DOI: 10.1038/s41598-024-59077-5] [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: 04/10/2023] [Accepted: 04/07/2024] [Indexed: 04/12/2024] Open
Abstract
Rib fractures are highly predictive of non-accidental trauma in children under 3 years old. Rib fracture detection in pediatric radiographs is challenging because fractures can be obliquely oriented to the imaging detector, obfuscated by other structures, incomplete, and non-displaced. Prior studies have shown up to two-thirds of rib fractures may be missed during initial interpretation. In this paper, we implemented methods for improving the sensitivity (i.e. recall) performance for detecting and localizing rib fractures in pediatric chest radiographs to help augment performance of radiology interpretation. These methods adapted two convolutional neural network (CNN) architectures, RetinaNet and YOLOv5, and our previously proposed decision scheme, "avalanche decision", that dynamically reduces the acceptance threshold for proposed regions in each image. Additionally, we present contributions of using multiple image pre-processing and model ensembling techniques. Using a custom dataset of 1109 pediatric chest radiographs manually labeled by seven pediatric radiologists, we performed 10-fold cross-validation and reported detection performance using several metrics, including F2 score which summarizes precision and recall for high-sensitivity tasks. Our best performing model used three ensembled YOLOv5 models with varied input processing and an avalanche decision scheme, achieving an F2 score of 0.725 ± 0.012. Expert inter-reader performance yielded an F2 score of 0.732. Results demonstrate that our combination of sensitivity-driving methods provides object detector performance approaching the capabilities of expert human readers, suggesting that these methods may provide a viable approach to identify all rib fractures.
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Affiliation(s)
| | | | | | | | - Joseph Junewick
- Michigan State University, East Lansing, MI, 48823, USA
- Helen DeVos Children's Hospital, Grand Rapids, MI, USA
- Advanced Radiology Services, Grand Rapids, MI, USA
| | - Andy Zbojniewicz
- Michigan State University, East Lansing, MI, 48823, USA
- Helen DeVos Children's Hospital, Grand Rapids, MI, USA
- Advanced Radiology Services, Grand Rapids, MI, USA
| | | | | | - Jamie Frost
- Michigan State University, East Lansing, MI, 48823, USA
- Helen DeVos Children's Hospital, Grand Rapids, MI, USA
- Advanced Radiology Services, Grand Rapids, MI, USA
| | - Adam Alessio
- Michigan State University, East Lansing, MI, 48823, USA.
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Bi L, Buehner U, Fu X, Williamson T, Choong P, Kim J. Hybrid CNN-transformer network for interactive learning of challenging musculoskeletal images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107875. [PMID: 37871450 DOI: 10.1016/j.cmpb.2023.107875] [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: 08/06/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 10/25/2023]
Abstract
BACKGROUND AND OBJECTIVES Segmentation of regions of interest (ROIs) such as tumors and bones plays an essential role in the analysis of musculoskeletal (MSK) images. Segmentation results can help with orthopaedic surgeons in surgical outcomes assessment and patient's gait cycle simulation. Deep learning-based automatic segmentation methods, particularly those using fully convolutional networks (FCNs), are considered as the state-of-the-art. However, in scenarios where the training data is insufficient to account for all the variations in ROIs, these methods struggle to segment the challenging ROIs that with less common image characteristics. Such characteristics might include low contrast to the background, inhomogeneous textures, and fuzzy boundaries. METHODS we propose a hybrid convolutional neural network - transformer network (HCTN) for semi-automatic segmentation to overcome the limitations of segmenting challenging MSK images. Specifically, we propose to fuse user-inputs (manual, e.g., mouse clicks) with high-level semantic image features derived from the neural network (automatic) where the user-inputs are used in an interactive training for uncommon image characteristics. In addition, we propose to leverage the transformer network (TN) - a deep learning model designed for handling sequence data, in together with features derived from FCNs for segmentation; this addresses the limitation of FCNs that can only operate on small kernels, which tends to dismiss global context and only focus on local patterns. RESULTS We purposely selected three MSK imaging datasets covering a variety of structures to evaluate the generalizability of the proposed method. Our semi-automatic HCTN method achieved a dice coefficient score (DSC) of 88.46 ± 9.41 for segmenting the soft-tissue sarcoma tumors from magnetic resonance (MR) images, 73.32 ± 11.97 for segmenting the osteosarcoma tumors from MR images and 93.93 ± 1.84 for segmenting the clavicle bones from chest radiographs. When compared to the current state-of-the-art automatic segmentation method, our HCTN method is 11.7%, 19.11% and 7.36% higher in DSC on the three datasets, respectively. CONCLUSION Our experimental results demonstrate that HCTN achieved more generalizable results than the current methods, especially with challenging MSK studies.
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Affiliation(s)
- Lei Bi
- Institute of Translational Medicine, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China; School of Computer Science, University of Sydney, NSW, Australia
| | | | - Xiaohang Fu
- School of Computer Science, University of Sydney, NSW, Australia
| | - Tom Williamson
- Stryker Corporation, Kalamazoo, Michigan, USA; Centre for Additive Manufacturing, School of Engineering, RMIT University, VIC, Australia
| | - Peter Choong
- Department of Surgery, University of Melbourne, VIC, Australia
| | - Jinman Kim
- School of Computer Science, University of Sydney, NSW, Australia.
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5
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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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Lee KC, Choi IC, Kang CH, Ahn KS, Yoon H, Lee JJ, Kim BH, Shim E. Clinical Validation of an Artificial Intelligence Model for Detecting Distal Radius, Ulnar Styloid, and Scaphoid Fractures on Conventional Wrist Radiographs. Diagnostics (Basel) 2023; 13:diagnostics13091657. [PMID: 37175048 PMCID: PMC10178713 DOI: 10.3390/diagnostics13091657] [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: 03/30/2023] [Revised: 05/02/2023] [Accepted: 05/06/2023] [Indexed: 05/15/2023] Open
Abstract
This study aimed to assess the feasibility and performance of an artificial intelligence (AI) model for detecting three common wrist fractures: distal radius, ulnar styloid process, and scaphoid. The AI model was trained with a dataset of 4432 images containing both fractured and non-fractured wrist images. In total, 593 subjects were included in the clinical test. Two human experts independently diagnosed and labeled the fracture sites using bounding boxes to build the ground truth. Two novice radiologists also performed the same task, both with and without model assistance. The sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated for each wrist location. The AUC for detecting distal radius, ulnar styloid, and scaphoid fractures per wrist were 0.903 (95% C.I. 0.887-0.918), 0.925 (95% C.I. 0.911-0.939), and 0.808 (95% C.I. 0.748-0.967), respectively. When assisted by the AI model, the scaphoid fracture AUC of the two novice radiologists significantly increased from 0.75 (95% C.I. 0.66-0.83) to 0.85 (95% C.I. 0.77-0.93) and from 0.71 (95% C.I. 0.62-0.80) to 0.80 (95% C.I. 0.71-0.88), respectively. Overall, the developed AI model was found to be reliable for detecting wrist fractures, particularly for scaphoid fractures, which are commonly missed.
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Affiliation(s)
- Kyu-Chong Lee
- Department of Radiology, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - In Cheul Choi
- Department of Orthopedics Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Chang Ho Kang
- Department of Radiology, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Kyung-Sik Ahn
- Department of Radiology, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Heewon Yoon
- Department of Radiology, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | | | - Baek Hyun Kim
- Department of Radiology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
| | - Euddeum Shim
- Department of Radiology, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
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Hasan F, Mudey A, Joshi A. Role of Internet of Things (IoT), Artificial Intelligence and Machine Learning in Musculoskeletal Pain: A Scoping Review. Cureus 2023; 15:e37352. [PMID: 37182066 PMCID: PMC10170184 DOI: 10.7759/cureus.37352] [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: 01/25/2023] [Accepted: 04/09/2023] [Indexed: 05/16/2023] Open
Abstract
Artificial intelligence (AI), Internet of Things (IoT), and machine learning (ML) have considerably increased in numerous critical medical sectors and significantly impacted our daily lives. Digital health interventions support cost-effective, accessible, and preferred interventions that meet time and resource constraints for large patient populations. Musculoskeletal conditions significantly impact society, the economy, and people's life. Adults with chronic neck and back pain are frequently the victims, rendering them physically unable to move. They often experience discomfort, necessitating them to take over-the-counter medications or painkilling gels. Technologies driven by AI have been suggested as an alternative approach to improve adherence to exercise therapy, which in turn helps patients undertake exercises every day to relieve pain associated with the musculoskeletal system. Even though there are many computer-aided evaluations available for physiotherapy rehabilitation, current approaches to computer-aided performance and monitoring lack flexibility and robustness. A thorough literature search was conducted using key databases like PubMed and Google Scholar, as well as Medical Subject Headings (MeSH) terms and related keywords. This research aimed to determine if AI-operated digital health therapies that use cutting-edge IoT, brain imaging, and ML technologies are beneficial in lowering pain and enhancing functional impairment in patients with musculoskeletal diseases. The secondary goal was to ascertain whether solutions driven by machine learning or artificial intelligence can improve exercise compliance and be viewed as a lifestyle choice.
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Affiliation(s)
- Fatima Hasan
- Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND
| | - Abhay Mudey
- Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND
| | - Abhishek Joshi
- Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND
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Kunze KN, Jang SJ, Li T, Mayman DA, Vigdorchik JM, Jerabek SA, Fragomen AT, Sculco PK. Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population-level analysis using deep learning. Knee Surg Sports Traumatol Arthrosc 2023; 31:586-595. [PMID: 36367544 DOI: 10.1007/s00167-022-07213-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/22/2022] [Indexed: 11/13/2022]
Abstract
PURPOSE To (1) develop a deep-learning (DL) algorithm capable of producing limb-length and knee-alignment measurements, and (2) determine the association between limb-length discrepancy (LLD), coronal-plane alignment, osteoarthritis (OA) severity, and patient-reported knee pain. METHODS A multicenter, prospective patient cohort from the Osteoarthritis Initiative between 2004 and 2015 with full-limb standing radiographs at 12 month follow-up was included. A convolutional neural network was developed to automate measurements of the hip-knee-ankle (HKA) angle, femur, and tibia lengths, and LLD. At 12 month follow-up, patients reported their frequency of knee pain since enrollment and current level of knee pain. RESULTS A total of 1011 patients (2022 knees, 52.3% female) with an average age of 61.2 ± 9.0 years were included. The algorithm performed 12,312 measurements in 5.4 h. ICC values of HKA and LLD ranged between 0.87 and 1.00 when compared against trained radiologist measurements. Knees producing pain most days of the month were significantly more varus (mean HKA:- 3.9° ± 2.8°) or valgus (mean HKA:2.8° ± 2.3°) compared to knees that did not produce any pain (p < 0.05). In varus knees, those producing pain on most days were part of the shorter limb compared to nonpainful knees (p < 0.05). Baseline Kellgren-Lawrence grade was significantly associated with HKA magnitude, LLD, and pain frequency at 12 month follow-up (p < 0.05 all). CONCLUSION A higher frequency of knee pain was associated with more severe coronal plane deformity, with valgus deviation being one degree less than varus on average, suggesting that the knee tolerates less valgus deformation before symptoms become more consistent. Knee pain frequency was also associated with greater LLD and baseline KL grade, suggesting an association between radiographically apparent joint degeneration and pain frequency. LEVEL OF EVIDENCE IV case series.
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Affiliation(s)
- Kyle N Kunze
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
| | - Seong Jun Jang
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.,Weill Cornell College of Medicine, New York, NY, USA
| | - Tim Li
- Weill Cornell College of Medicine, New York, NY, USA
| | - David A Mayman
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.,Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Jonathan M Vigdorchik
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.,Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Seth A Jerabek
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.,Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
| | - Austin T Fragomen
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.,Limb Lengthening and Complex Reconstruction Service, Hospital for Special Surgery, New York, NY, USA
| | - Peter K Sculco
- Department of Orthopedic Surgery, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.,Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA
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9
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Chang CY, Huber FA, Yeh KJ, Buckless C, Torriani M. Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs. Skeletal Radiol 2023; 52:1377-1384. [PMID: 36651936 DOI: 10.1007/s00256-023-04283-x] [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: 11/03/2022] [Revised: 01/11/2023] [Accepted: 01/11/2023] [Indexed: 01/19/2023]
Abstract
OBJECTIVE To develop, train, and test a convolutional neural network (CNN) for detection of spinal lytic lesions in chest, abdomen, and pelvis CT scans. MATERIALS AND METHODS Cases of malignant spinal lytic lesions in CT scans were identified. Images were manually segmented for the following classes: (i) lesion, (ii) normal bone, (iii) background. If more than one lesion was on a single slice, all lesions were segmented. Images were stored as 128×128 pixel grayscale, with 10% segregated for testing. The training pipeline of the dataset included histogram equalization and data augmentation. A model was trained on Keras/Tensorflow using an 80/20 training/validation split, based on U-Net architecture. Additional testing of the model was performed on 1106 images of healthy controls. Global sensitivity measured detection of any lesion on a single image. Local sensitivity and positive predictive value (PPV) measured detection of all lesions on an image. Global specificity measured false positive rate in non-pathologic bone. RESULTS Six hundred images were obtained for model creation. The training set consisted of 540 images, which was augmented to 20,000. The test set consisted of 60 images. Model training was performed in triplicate. Mean Dice scores were 0.61 for lytic lesion, 0.95 for normal bone, and 0.99 for background. Mean global sensitivity was 90.6%, local sensitivity was 74.0%, local PPV was 78.3%, and global specificity was 63.3%. At least one false positive lesion was noted in 28.8-44.9% of control images. CONCLUSION A task-trained CNN showed good sensitivity in detecting spinal lytic lesions in axial CT images.
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Affiliation(s)
- Connie Y Chang
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA.
| | - Florian A Huber
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Kaitlyn J Yeh
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA
| | - Colleen Buckless
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA
| | - Martin Torriani
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street - YAW 6 -, Boston, MA, 02114, USA
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10
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Aisen AM, Rodrigues PS. Deep Learning to Detect Pancreatic Cancer at CT: Artificial Intelligence Living Up to Its Hype. Radiology 2023; 306:183-185. [PMID: 36098644 DOI: 10.1148/radiol.222126] [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)
- Alex M Aisen
- From Philips Healthcare, Cambridge, MA (A.M.A.); and Philips Healthcare, Best, the Netherlands (P.S.R.)
| | - Pedro S Rodrigues
- From Philips Healthcare, Cambridge, MA (A.M.A.); and Philips Healthcare, Best, the Netherlands (P.S.R.)
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11
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Aaltonen HL, O'Reilly MK, Linnau KF, Dong Q, Johnston SK, Jarvik JG, Cross NM. m2ABQ-a proposed refinement of the modified algorithm-based qualitative classification of osteoporotic vertebral fractures. Osteoporos Int 2023; 34:137-145. [PMID: 36336755 DOI: 10.1007/s00198-022-06546-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 08/29/2022] [Indexed: 11/09/2022]
Abstract
UNLABELLED Currently, there is no reproducible, widely accepted gold standard to classify osteoporotic vertebral body fractures (OVFs). The purpose of this study is to refine a method with clear rules to classify OVFs for machine learning purposes. The method was found to have moderate interobserver agreement that improved with training. INTRODUCTION The current methods to classify osteoporotic vertebral body fractures are considered ambiguous; there is no reproducible, accepted gold standard. The purpose of this study is to refine classification methodology by introducing clear, unambiguous rules and a refined flowchart to allow consistent classification of osteoporotic vertebral body fractures. METHODS We developed a set of rules and refinements that we called m2ABQ to classify vertebrae into five categories. A fracture-enriched database of thoracic and lumbar spine radiographs of patients 65 years of age and older was retrospectively obtained from clinical institutional radiology records using natural language processing. Five raters independently classified each vertebral body using the m2ABQ system. After each annotation round, consensus sessions that included all raters were held to discuss and finalize a consensus annotation for each vertebral body where individual raters' evaluations differed. This process led to further refinement and development of the rules. RESULTS Each annotation round showed increase in Fleiss kappa both for presence vs absence of fracture 0.62 (0.56-0.68) to 0.70 (0.65-0.75), as well as for the whole m2ABQ scale 0.29 (0.25-0.33) to 0.54 (0.51-0.58). CONCLUSION The m2ABQ system demonstrates moderate interobserver agreement and practical feasibility for classifying osteoporotic vertebral body fractures. Future studies to compare the method to existing studies are warranted, as well as further development of its use in machine learning purposes.
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Affiliation(s)
- H L Aaltonen
- Department of Radiology, University of Washington, Seattle, WA, USA.
- Department of Medical Imaging and Physiology, Lund University, Malmo, Sweden.
| | - M K O'Reilly
- Department of Radiology, University of Washington, Seattle, WA, USA
- Department of Radiology, University of Limerick Hospital Group, Limerick, Ireland
- Clinical Learning, Evidence, And Research [CLEAR] Center for Musculoskeletal Disorders, Seattle, USA
| | - K F Linnau
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Q Dong
- Clinical Learning, Evidence, And Research [CLEAR] Center for Musculoskeletal Disorders, Seattle, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - S K Johnston
- Department of Radiology, University of Washington, Seattle, WA, USA
- Clinical Learning, Evidence, And Research [CLEAR] Center for Musculoskeletal Disorders, Seattle, USA
| | - J G Jarvik
- Department of Radiology, University of Washington, Seattle, WA, USA
- Clinical Learning, Evidence, And Research [CLEAR] Center for Musculoskeletal Disorders, Seattle, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - N M Cross
- Department of Radiology, University of Washington, Seattle, WA, USA
- Clinical Learning, Evidence, And Research [CLEAR] Center for Musculoskeletal Disorders, Seattle, USA
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12
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Makrogiannis S, Okorie A, Di Iorio A, Bandinelli S, Ferrucci L. Multi-atlas segmentation and quantification of muscle, bone and subcutaneous adipose tissue in the lower leg using peripheral quantitative computed tomography. Front Physiol 2022; 13:951368. [PMID: 36311235 PMCID: PMC9614313 DOI: 10.3389/fphys.2022.951368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/26/2022] [Indexed: 11/26/2022] Open
Abstract
Accurate and reproducible tissue identification is essential for understanding structural and functional changes that may occur naturally with aging, or because of a chronic disease, or in response to intervention therapies. Peripheral quantitative computed tomography (pQCT) is regularly employed for body composition studies, especially for the structural and material properties of the bone. Furthermore, pQCT acquisition requires low radiation dose and the scanner is compact and portable. However, pQCT scans have limited spatial resolution and moderate SNR. pQCT image quality is frequently degraded by involuntary subject movement during image acquisition. These limitations may often compromise the accuracy of tissue quantification, and emphasize the need for automated and robust quantification methods. We propose a tissue identification and quantification methodology that addresses image quality limitations and artifacts, with increased interest in subject movement. We introduce a multi-atlas image segmentation (MAIS) framework for semantic segmentation of hard and soft tissues in pQCT scans at multiple levels of the lower leg. We describe the stages of statistical atlas generation, deformable registration and multi-tissue classifier fusion. We evaluated the performance of our methodology using multiple deformable registration approaches against reference tissue masks. We also evaluated the performance of conventional model-based segmentation against the same reference data to facilitate comparisons. We studied the effect of subject movement on tissue segmentation quality. We also applied the top performing method to a larger out-of-sample dataset and report the quantification results. The results show that multi-atlas image segmentation with diffeomorphic deformation and probabilistic label fusion produces very good quality over all tissues, even for scans with significant quality degradation. The application of our technique to the larger dataset reveals trends of age-related body composition changes that are consistent with the literature. Because of its robustness to subject motion artifacts, our MAIS methodology enables analysis of larger number of scans than conventional state-of-the-art methods. Automated analysis of both soft and hard tissues in pQCT is another contribution of this work.
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Affiliation(s)
- Sokratis Makrogiannis
- Math Imaging and Visual Computing Lab, Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, Dover, DE, United States
- *Correspondence: Sokratis Makrogiannis,
| | - Azubuike Okorie
- Math Imaging and Visual Computing Lab, Division of Physics, Engineering, Mathematics and Computer Science, Delaware State University, Dover, DE, United States
| | - Angelo Di Iorio
- Antalgic Mini-invasive and Rehab-Outpatients Unit, Department of Innovative Technologies in Medicine & Dentistry, University “G.d’Annunzio”, Chieti-Pescara, Italy
| | | | - Luigi Ferrucci
- National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
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13
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Lentle BC, Prior JC. Osteoporotic vertebral fracture (OVF): diagnosis requires an informed observer. Osteoporos Int 2022; 33:1409-1410. [PMID: 35352143 DOI: 10.1007/s00198-021-06287-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 12/21/2021] [Indexed: 10/18/2022]
Affiliation(s)
- B C Lentle
- BC Centre of the Canadian Multicentre Osteoporosis Study (CaMos), Vancouver, Canada.
- Department of Radiology, The University of British Columbia, 205 Kimta Rd., 740, Victoria, British Columbia, V9A 6T5, Canada.
| | - J C Prior
- BC Centre of the Canadian Multicentre Osteoporosis Study (CaMos), Vancouver, Canada
- Centre for Menstrual Cycle and Ovulation Research, Endocrinology and Metabolism, Department of Medicine, The University of British Columbia, Room 4111-2775 Laurel Street, Vancouver, British Columbia, V5Z 1M9, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
- BC Women's Health Research Institute, Vancouver, Canada
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14
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Khalil YA, Becherucci EA, Kirschke JS, Karampinos DC, Breeuwer M, Baum T, Sollmann N. Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database. Sci Data 2022; 9:97. [PMID: 35322028 PMCID: PMC8943029 DOI: 10.1038/s41597-022-01222-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 03/03/2022] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance imaging (MRI) is widely utilized for diagnosing and monitoring of spinal disorders. For a number of applications, particularly those related to quantitative MRI, an essential step towards achieving reliable and objective measurements is the segmentation of the examined structures. Performed manually, such process is time-consuming and prone to errors, posing a bottleneck to its clinical applicability. A more efficient analysis would be achieved by automating a segmentation process. However, routine spine MRI acquisitions pose several challenges for achieving robust and accurate segmentations, due to varying MRI acquisition characteristics occurring in data acquired from different sites. Moreover, heterogeneous annotated datasets, collected from multiple scanners with different pulse sequence protocols, are limited. Thus, we present a manually segmented lumbar spine MRI database containing a wide range of data obtained from multiple scanners and pulse sequences, with segmentations of lumbar vertebral bodies and intervertebral discs. The database is intended for the use in developing and testing of automated lumbar spine segmentation algorithms in multi-domain scenarios. Measurement(s) | Vertebral Body • Intervertebral Disc | Technology Type(s) | Magnetic Resonance Imaging |
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Affiliation(s)
- Yasmina Al Khalil
- Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Edoardo A Becherucci
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marcel Breeuwer
- Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. .,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany. .,Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany. .,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
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15
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Ajmera P, Kharat A, Botchu R, Gupta H, Kulkarni V. Real-world analysis of artificial intelligence in musculoskeletal trauma. J Clin Orthop Trauma 2021; 22:101573. [PMID: 34527511 PMCID: PMC8427222 DOI: 10.1016/j.jcot.2021.101573] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/20/2021] [Accepted: 08/20/2021] [Indexed: 11/30/2022] Open
Abstract
Musculoskeletal trauma accounts for a large percentage of emergency room visits and is amongst the top causes of unscheduled patient visits to the emergency room. Musculoskeletal trauma results in expenditure of billions of dollars and protracted losses of quality-adjusted life years. New and innovative methods are needed to minimise the impact by ensuring quick and accurate assessment. However, each of the currently utilised radiological procedures, such as radiography, ultrasonography, computed tomography, and magnetic resonance imaging, has resulted in implosion of medical imaging data. Deep learning, a recent advancement in artificial intelligence, has demonstrated the potential to analyse medical images with sensitivity and specificity at par with experts. In this review article, we intend to summarise and showcase the various developments which have occurred in the dynamic field of artificial intelligence and machine learning and how their applicability to different aspects of imaging in trauma can be explored to improvise our existing reporting systems and improvise on patient outcomes.
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Affiliation(s)
- Pranav Ajmera
- Department of Radiology, Dr D.Y. Patil Medical College, Hospital and Research Center, DPU, Pune, India
| | - Amit Kharat
- Department of Radiology, Dr D.Y. Patil Medical College, Hospital and Research Center, DPU, Pune, India
| | - Rajesh Botchu
- Department of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham, UK
| | - Harun Gupta
- Department of Musculoskeletal Radiology, Leeds Teaching Hospitals, Leeds, UK
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16
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Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements. Eur Radiol 2021; 32:1465-1474. [PMID: 34687347 PMCID: PMC8831336 DOI: 10.1007/s00330-021-08284-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/02/2021] [Accepted: 08/17/2021] [Indexed: 11/30/2022]
Abstract
Objectives To determine the accuracy of an artificial neural network (ANN) for fully automated detection of the presence and phase of iodinated contrast agent in routine abdominal multidetector computed tomography (MDCT) scans and evaluate the effect of contrast correction for osteoporosis screening. Methods This HIPPA-compliant study retrospectively included 579 MDCT scans in 193 patients (62.4 ± 14.6 years, 48 women). Three different ANN models (2D DenseNet with random slice selection, 2D DenseNet with anatomy-guided slice selection, 3D DenseNet) were trained in 462 MDCT scans of 154 patients (threefold cross-validation), who underwent triphasic CT. All ANN models were tested in 117 unseen triphasic scans of 39 patients, as well as in a public MDCT dataset containing 311 patients. In the triphasic test scans, trabecular volumetric bone mineral density (BMD) was calculated using a fully automated pipeline. Root-mean-square errors (RMSE) of BMD measurements with and without correction for contrast application were calculated in comparison to nonenhanced (NE) scans. Results The 2D DenseNet with anatomy-guided slice selection outperformed the competing models and achieved an F1 score of 0.98 and an accuracy of 98.3% in the test set (public dataset: F1 score 0.93; accuracy 94.2%). Application of contrast agent resulted in significant BMD biases (all p < .001; portal-venous (PV): RMSE 18.7 mg/ml, mean difference 17.5 mg/ml; arterial (AR): RMSE 6.92 mg/ml, mean difference 5.68 mg/ml). After the fully automated correction, this bias was no longer significant (p > .05; PV: RMSE 9.45 mg/ml, mean difference 1.28 mg/ml; AR: RMSE 3.98 mg/ml, mean difference 0.94 mg/ml). Conclusion Automatic detection of the contrast phase in multicenter CT data was achieved with high accuracy, minimizing the contrast-induced error in BMD measurements. Key Points • A 2D DenseNet with anatomy-guided slice selection achieved an F1 score of 0.98 and an accuracy of 98.3% in the test set. In a public dataset, an F1 score of 0.93 and an accuracy of 94.2% were obtained. • Automated adjustment for contrast injection improved the accuracy of lumbar bone mineral density measurements (RMSE 18.7 mg/ml vs. 9.45 mg/ml respectively, in the portal-venous phase). • An artificial neural network can reliably reveal the presence and phase of iodinated contrast agent in multidetector CT scans (https://github.com/ferchonavarro/anatomy_guided_contrast_c). This allows minimizing the contrast-induced error in opportunistic bone mineral density measurements.
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17
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Zapaishchykova A, Dreizin D, Li Z, Wu JY, Roohi SF, Unberath M. An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12903:424-433. [PMID: 37483538 PMCID: PMC10362989 DOI: 10.1007/978-3-030-87199-4_40] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Pelvic ring disruptions result from blunt injury mechanisms and are often found in patients with multi-system trauma. To grade pelvic fracture severity in trauma victims based on whole-body CT, the Tile AO/OTA classification is frequently used. Due to the high volume of whole-body trauma CTs generated in busy trauma centers, an automated approach to Tile classification would provide substantial value, e. g., to prioritize the reading queue of the attending trauma radiologist. In such scenario, an automated method should perform grading based on a transparent process and based on interpretable features to enable interaction with human readers and lower their workload by offering insights from a first automated read of the scan. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grade classification. The method operates similarly to human interpretation of CT scans and first detects distinct pelvic fractures on CT with high specificity using a Faster-RCNN model that are then interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. The Bayesian causal model and finally, the object detector are then queried for likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides finding location and type using the object detector, as well as information on important counterfactuals that would invalidate the system's recommendation and achieves an AUC of 83.3%/85.1% for translational/rotational instability. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box approaches.
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18
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Nissinen T, Suoranta S, Saavalainen T, Sund R, Hurskainen O, Rikkonen T, Kröger H, Lähivaara T, Väänänen SP. Detecting pathological features and predicting fracture risk from dual-energy X-ray absorptiometry images using deep learning. Bone Rep 2021; 14:101070. [PMID: 33997147 PMCID: PMC8102403 DOI: 10.1016/j.bonr.2021.101070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 04/14/2021] [Indexed: 11/08/2022] Open
Abstract
Dual-energy X-ray absorptiometry (DXA) is the gold standard imaging method for diagnosing osteoporosis in clinical practice. The DXA images are commonly used to estimate a numerical value for bone mineral density (BMD), which decreases in osteoporosis. Low BMD is a known risk factor for osteoporotic fractures. In this study, we used deep learning to identify lumbar scoliosis and structural abnormalities that potentially affect BMD but are often neglected in lumbar spine DXA analysis. In addition, we tested the approach's ability to predict fractures using only DXA images. A dataset of 2949 images gathered by Kuopio Osteoporosis Risk Factor and Prevention Study was used to train a convolutional neural network (CNN) for classification. The model was able to classify scoliosis with an AUC of 0.96 and structural abnormalities causing unreliable BMD measurement with an AUC of 0.91. It predicted fractures occurring within 5 years from the lumbar spine DXA scan with an AUC of 0.63, meeting the predictive performance of combined BMD measurements from the lumbar spine and hip. In an independent test set of 574 clinical patients, AUC for lumbar scoliosis was 0.93 and AUC for unreliable BMD measurements was 0.94. In each classification task, neural network visualizations indicated the model's predictive strategy. We conclude that deep learning could complement the well established DXA method for osteoporosis diagnostics by analyzing incidental findings and image reliability, and improve its predictive ability in the future.
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Affiliation(s)
- Tomi Nissinen
- Department of Applied Physics, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
- Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Sanna Suoranta
- Department of Clinical Radiology, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Taavi Saavalainen
- Department of Clinical Radiology, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Reijo Sund
- Institute of Clinical Medicine, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
| | - Ossi Hurskainen
- Department of Clinical Radiology, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Toni Rikkonen
- Institute of Clinical Medicine, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
| | - Heikki Kröger
- Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
| | - Timo Lähivaara
- Department of Applied Physics, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
| | - Sami P. Väänänen
- Department of Applied Physics, University of Eastern Finland, POB1627, 70211 Kuopio, Finland
- Department of Clinical Radiology, Kuopio University Hospital, POB1777, 70211 Kuopio, Finland
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19
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Smets J, Shevroja E, Hügle T, Leslie WD, Hans D. Machine Learning Solutions for Osteoporosis-A Review. J Bone Miner Res 2021; 36:833-851. [PMID: 33751686 DOI: 10.1002/jbmr.4292] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/04/2021] [Accepted: 03/16/2021] [Indexed: 12/11/2022]
Abstract
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Julien Smets
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Enisa Shevroja
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Didier Hans
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
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20
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Vogrin M, Trojner T, Kelc R. Artificial intelligence in musculoskeletal oncological radiology. Radiol Oncol 2020; 55:1-6. [PMID: 33885240 PMCID: PMC7877260 DOI: 10.2478/raon-2020-0068] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 09/29/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Due to the rarity of primary bone tumors, precise radiologic diagnosis often requires an experienced musculoskeletal radiologist. In order to make the diagnosis more precise and to prevent the overlooking of potentially dangerous conditions, artificial intelligence has been continuously incorporated into medical practice in recent decades. This paper reviews some of the most promising systems developed, including those for diagnosis of primary and secondary bone tumors, breast, lung and colon neoplasms. CONCLUSIONS Although there is still a shortage of long-term studies confirming its benefits, there is probably a considerable potential for further development of computer-based expert systems aiming at a more efficient diagnosis of bone and soft tissue tumors.
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Affiliation(s)
- Matjaz Vogrin
- Department of Orthopaedic Surgery, University Medical CenterMaribor, Slovenia
- Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Teodor Trojner
- Department of Orthopaedic Surgery, University Medical CenterMaribor, Slovenia
| | - Robi Kelc
- Department of Orthopaedic Surgery, University Medical CenterMaribor, Slovenia
- Faculty of Medicine, University of Maribor, Maribor, Slovenia
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21
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Cheng X, Yuan H, Cheng J, Weng X, Xu H, Gao J, Huang M, Wáng YXJ, Wu Y, Xu W, Liu L, Liu H, Huang C, Jin Z, Tian W. Chinese expert consensus on the diagnosis of osteoporosis by imaging and bone mineral density. Quant Imaging Med Surg 2020; 10:2066-2077. [PMID: 33014734 DOI: 10.21037/qims-2020-16] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
With an aging society, osteoporosis is one of the most common diseases threatening the health of China's elderly population and is an issue that is raising increasing concern. Osteoporosis is characterized by bone loss and increased susceptibility to fragility fractures. Various imaging modalities such as X-ray, CT, MRI and nuclear medicine along with assessment of bone mineral density (BMD) play an important role in its diagnosis and management, and the treatment requires multidisciplinary teamwork. A lack of consensus in the approach to imaging and BMD measurement is hampering the quality of service and patient care in China. Therefore a panel of Chinese experts from the fields of radiology, orthopedics, endocrinology and nuclear medicine reviewed the international guidelines, consensus and literature with the most recent data from China and, taking account of current clinical practice in China, the panel reached this consensus to help guide the diagnosis of osteoporosis using imaging and BMD. This consensus report provides guidelines and standards for the imaging and BMD assessment of osteoporosis and criteria for the diagnosis of osteoporosis in China.
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Affiliation(s)
- Xiaoguang Cheng
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Jingliang Cheng
- Department of MRI, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xisheng Weng
- Department of Orthopedics, Peking Union Medical College Hospital, Beijing, China
| | - Hao Xu
- Department of Nuclear Medicine, First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Jianbo Gao
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingqian Huang
- Department of Radiology, Mount Sinai Hospital, New York, USA
| | - Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Yan Wu
- Department of Radiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenjian Xu
- Department of Radiology, The Affiliated hospital of Qingdao University, Qingdao, China
| | - Li Liu
- Forensic Medical Examination Center of Beijing Public Security Bureau, Beijing, China
| | - Hua Liu
- Forensic Medical Examination Center of Beijing Public Security Bureau, Beijing, China
| | - Chen Huang
- Department of orthopedics, Yantaishan Hospital, Yantai, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Wei Tian
- Department of Spine Surgery, Beijing Jishuitan Hospital, Beijing, China
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Gorelik N, Gyftopoulos S. Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report. Can Assoc Radiol J 2020; 72:45-59. [PMID: 32809857 DOI: 10.1177/0846537120947148] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. This article explores the impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the generation of the report, with an added Canadian perspective. Noninterpretive tasks which may be assisted by AI include the ordering of appropriate imaging tests, automatic exam protocoling, optimized scheduling, shorter magnetic resonance imaging acquisition time, computed tomography imaging with reduced artifact and radiation dose, and new methods of generation and utilization of radiology reports. Applications of AI for image interpretation consist of the determination of bone age, body composition measurements, screening for osteoporosis, identification of fractures, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis.
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Affiliation(s)
- Natalia Gorelik
- Department of Diagnostic Radiology, 54473McGill University Health Center, Montreal, Quebec, Canada
| | - Soterios Gyftopoulos
- Department of Radiology, 12297NYU Langone Medical Center/NYU Langone Orthopedic Center, New York, NY, USA.,Department of Orthopedic Surgery, 12297NYU Langone Medical Center/NYU Langone Orthopedic Center, New York, NY, USA
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Abstract
Artificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.
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Affiliation(s)
- Narges Razavian
- Department of Radiology and Population Health, NYU Langone Health and NYU Center for Data Science, New York, New York
| | - Florian Knoll
- Department of Radiology, NYU Langone Health, New York, New York
| | - Krzysztof J. Geras
- Department of Radiology, NYU Langone Health and NYU Center for Data Science, New York, New York
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