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Farhadi F, Barnes MR, Sugito HR, Sin JM, Henderson ER, Levy JJ. Applications of artificial intelligence in orthopaedic surgery. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:995526. [PMID: 36590152 PMCID: PMC9797865 DOI: 10.3389/fmedt.2022.995526] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
The practice of medicine is rapidly transforming as a result of technological breakthroughs. Artificial intelligence (AI) systems are becoming more and more relevant in medicine and orthopaedic surgery as a result of the nearly exponential growth in computer processing power, cloud based computing, and development, and refining of medical-task specific software algorithms. Because of the extensive role of technologies such as medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to management of orthopaedic disorders, the field is particularly ripe for the application of machine-based integration of imaging studies, among other applications. Through this review, we seek to promote awareness in the orthopaedics community of the current accomplishments and projected uses of AI and ML as described in the literature. We summarize the current state of the art in the use of ML and AI in five key orthopaedic disciplines: joint reconstruction, spine, orthopaedic oncology, trauma, and sports medicine.
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Affiliation(s)
- Faraz Farhadi
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States,Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, United States,Correspondence: Faraz Farhadi Joshua J. Levy
| | - Matthew R. Barnes
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Harun R. Sugito
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Jessica M. Sin
- Department of Radiology, Dartmouth Health, Lebanon, United States
| | - Eric R. Henderson
- Department of Orthopaedics, Dartmouth Health, Lebanon, United States
| | - Joshua J. Levy
- Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH, United States,Correspondence: Faraz Farhadi Joshua J. Levy
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Federer SJ, Jones GG. Artificial intelligence in orthopaedics: A scoping review. PLoS One 2021; 16:e0260471. [PMID: 34813611 PMCID: PMC8610245 DOI: 10.1371/journal.pone.0260471] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 11/11/2021] [Indexed: 11/19/2022] Open
Abstract
There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946-2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.
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Affiliation(s)
- Simon J. Federer
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, United Kingdom
- * E-mail:
| | - Gareth G. Jones
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, United Kingdom
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Merali Z, Wang JZ, Badhiwala JH, Witiw CD, Wilson JR, Fehlings MG. A deep learning model for detection of cervical spinal cord compression in MRI scans. Sci Rep 2021; 11:10473. [PMID: 34006910 PMCID: PMC8131597 DOI: 10.1038/s41598-021-89848-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/18/2021] [Indexed: 12/19/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volume of medical imaging data and provide initial interpretation of images gathered in a primary-care setting. We aimed to develop and validate a deep learning model for detection of cervical spinal cord compression in MRI scans. Patients undergoing surgery for DCM as a part of the AO Spine CSM-NA or CSM-I prospective cohort studies were included in our study. Patients were divided into a training/validation or holdout dataset. Images were labelled by two specialist physicians. We trained a deep convolutional neural network using images from the training/validation dataset and assessed model performance on the holdout dataset. The training/validation cohort included 201 patients with 6588 images and the holdout dataset included 88 patients with 2991 images. On the holdout dataset the deep learning model achieved an overall AUC of 0.94, sensitivity of 0.88, specificity of 0.89, and f1-score of 0.82. This model could improve the efficiency and objectivity of the interpretation of cervical spine MRI scans.
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Affiliation(s)
- Zamir Merali
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College Street, Toronto, ON, M5T 1P5, Canada
| | - Justin Z Wang
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College Street, Toronto, ON, M5T 1P5, Canada
| | - Jetan H Badhiwala
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College Street, Toronto, ON, M5T 1P5, Canada
| | - Christopher D Witiw
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College Street, Toronto, ON, M5T 1P5, Canada
- Division of Neurosurgery, St. Michael's Hospital, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
| | - Jefferson R Wilson
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College Street, Toronto, ON, M5T 1P5, Canada
- Division of Neurosurgery, St. Michael's Hospital, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
| | - Michael G Fehlings
- Division of Neurosurgery, Department of Surgery, University of Toronto, 149 College Street, Toronto, ON, M5T 1P5, Canada.
- Division of Neurosurgery, Krembil Neuroscience Centre, University Health Network, 399 Bathurst Street, Suite 4W-449, Toronto, ON, M5T 2S8, Canada.
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Merali ZA, Colak E, Wilson JR. Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions. Global Spine J 2021; 11:23S-29S. [PMID: 33890805 PMCID: PMC8076811 DOI: 10.1177/2192568220961353] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES We aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders. METHODS A literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included. RESULTS Within spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology. CONCLUSION Although substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.
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Affiliation(s)
- Zamir A. Merali
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Errol Colak
- Department of Medical Imaging, University of Toronto, St. Michael’s Hospital, 30 Bond St, Toronto, ON, M5B 1W8, Canada
| | - Jefferson R. Wilson
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Neurosurgery, St. Michael’s Hospital, Toronto, Ontario, Canada
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Peña-Solórzano CA, Albrecht DW, Bassed RB, Burke MD, Dimmock MR. Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting. Forensic Sci Int 2020; 316:110538. [PMID: 33120319 PMCID: PMC7568766 DOI: 10.1016/j.forsciint.2020.110538] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 04/28/2020] [Accepted: 10/04/2020] [Indexed: 12/18/2022]
Abstract
Machine learning (ML) techniques are increasingly being used in clinical medical imaging to automate distinct processing tasks. In post-mortem forensic radiology, the use of these algorithms presents significant challenges due to variability in organ position, structural changes from decomposition, inconsistent body placement in the scanner, and the presence of foreign bodies. Existing ML approaches in clinical imaging can likely be transferred to the forensic setting with careful consideration to account for the increased variability and temporal factors that affect the data used to train these algorithms. Additional steps are required to deal with these issues, by incorporating the possible variability into the training data through data augmentation, or by using atlases as a pre-processing step to account for death-related factors. A key application of ML would be then to highlight anatomical and gross pathological features of interest, or present information to help optimally determine the cause of death. In this review, we highlight results and limitations of applications in clinical medical imaging that use ML to determine key implications for their application in the forensic setting.
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Affiliation(s)
- Carlos A Peña-Solórzano
- Department of Medical Imaging and Radiation Sciences, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - David W Albrecht
- Clayton School of Information Technology, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - Richard B Bassed
- Victorian Institute of Forensic Medicine, 57-83 Kavanagh St., Southbank, Melbourne, VIC 3006, Australia; Department of Forensic Medicine, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - Michael D Burke
- Victorian Institute of Forensic Medicine, 57-83 Kavanagh St., Southbank, Melbourne, VIC 3006, Australia; Department of Forensic Medicine, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
| | - Matthew R Dimmock
- Department of Medical Imaging and Radiation Sciences, Monash University, Wellington Rd, Clayton, Melbourne, VIC 3800, Australia.
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Gao F, Liu S, Zhang X, Wang X, Zhang J. Automated Grading of Lumbar Disc Degeneration Using a Push-Pull Regularization Network Based on MRI. J Magn Reson Imaging 2020; 53:799-806. [PMID: 33094867 DOI: 10.1002/jmri.27400] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 10/01/2020] [Accepted: 10/02/2020] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Lower back pain is one of the most widely experienced health problems and is strongly associated with intervertebral disc (IVD) degeneration. The quantification of the degeneration is valuable for estimation of the material properties of the IVDs and then to perform biomechanical simulation of the spinal conditions and treatments. The MR image characteristics of relatively high intraclass variability and small interclass differences pose challenges for the classification algorithm to perform automatic grading of degenerated IVD. PURPOSE To assess the feasibility and improvement of a computer-assisted IVD degeneration grading method based on proposed push-pull regularization (PPR) strategy. STUDY TYPE Retrospective. POPULATION In total, 500 subjects (350 for training, 70 for validation, and 80 for test in a 10-time 10-fold cross validation setting) with varied lumbar disorders were included. FIELD STRENGTH/SEQUENCE 3.0T; T2 -weighted spin echo sequence. ASSESSMENT IVD degeneration grading was taken as a classification task of five classes according to the Pfirrmann grading system in this study. The classification results of deep-learning models with and without PPR were compared with the classifications made by three experienced spinal radiologists. STATISTICAL TESTS Paired t-tests. RESULTS The classification results show that in four classical CNN models of VGG-M, VGG-16, GoogleNet, and ResNet-34, by embedding a PPR strategy, the accuracies of grade II and III IVD classification were improved by more than 10% (P < 0.05), and that the overall accuracy (grades I to V) was improved by over 8% (P < 0.05). DATA CONCLUSION The embedded PPR significantly improved the classification performance, which enhanced CNN representation capability for IVD degeneration grading. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Fei Gao
- College of Engineering, Peking University, Beijing, China
| | - Shui Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jue Zhang
- College of Engineering, Peking University, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
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Galbusera F, Casaroli G, Bassani T. Artificial intelligence and machine learning in spine research. JOR Spine 2019; 2:e1044. [PMID: 31463458 PMCID: PMC6686793 DOI: 10.1002/jsp2.1044] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 01/31/2019] [Accepted: 01/31/2019] [Indexed: 12/21/2022] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing several industrial and research fields like computer vision, autonomous driving, natural language processing, and speech recognition. These novel tools are already having a major impact in radiology, diagnostics, and many other fields in which the availability of automated solution may benefit the accuracy and repeatability of the execution of critical tasks. In this narrative review, we first present a brief description of the various techniques that are being developed nowadays, with special focus on those used in spine research. Then, we describe the applications of AI and ML to problems related to the spine which have been published so far, including the localization of vertebrae and discs in radiological images, image segmentation, computer-aided diagnosis, prediction of clinical outcomes and complications, decision support systems, content-based image retrieval, biomechanics, and motion analysis. Finally, we briefly discuss major ethical issues related to the use of AI in healthcare, namely, accountability, risk of biased decisions as well as data privacy and security, which are nowadays being debated in the scientific community and by regulatory agencies.
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Affiliation(s)
- Fabio Galbusera
- Laboratory of Biological Structures MechanicsIRCCS Istituto Ortopedico GaleazziMilanItaly
| | - Gloria Casaroli
- Laboratory of Biological Structures MechanicsIRCCS Istituto Ortopedico GaleazziMilanItaly
| | - Tito Bassani
- Laboratory of Biological Structures MechanicsIRCCS Istituto Ortopedico GaleazziMilanItaly
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Fu W, Hao S, Wang M. Active learning on anchorgraph with an improved transductive experimental design. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.06.046] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation. Comput Med Imaging Graph 2015; 46 Pt 1:11-19. [PMID: 26060085 DOI: 10.1016/j.compmedimag.2015.05.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Revised: 04/15/2015] [Accepted: 05/04/2015] [Indexed: 11/23/2022]
Abstract
Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a 'normal' shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R=0.73) was achieved between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07±1.00mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation.
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Automated Radiological Grading of Spinal MRI. RECENT ADVANCES IN COMPUTATIONAL METHODS AND CLINICAL APPLICATIONS FOR SPINE IMAGING 2015. [DOI: 10.1007/978-3-319-14148-0_11] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Oktay AB, Albayrak NB, Akgul YS. Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images. Comput Med Imaging Graph 2014; 38:613-9. [PMID: 24972858 DOI: 10.1016/j.compmedimag.2014.04.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Revised: 04/02/2014] [Accepted: 04/30/2014] [Indexed: 10/25/2022]
Abstract
This paper presents a novel method for the automated diagnosis of the degenerative intervertebral disc disease in midsagittal MR images. The approach is based on combining distinct disc features under a machine learning framework. The discs in the lumbar MR images are first localized and segmented. Then, intensity, shape, context, and texture features of the discs are extracted with various techniques. A Support Vector Machine classifier is applied to classify the discs as normal or degenerated. The method is tested and validated on a clinical lumbar spine dataset containing 102 subjects and the results are comparable to the state of the art.
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Affiliation(s)
- Ayse Betul Oktay
- Department of Computer Engineering, Istanbul Medeniyet University, Istanbul, Turkey.
| | - Nur Banu Albayrak
- GIT Vision Lab, http://vision.gyte.edu.tr/, Department of Computer Engineering, Gebze Institute of Technology, Gebze, Kocaeli, Turkey.
| | - Yusuf Sinan Akgul
- GIT Vision Lab, http://vision.gyte.edu.tr/, Department of Computer Engineering, Gebze Institute of Technology, Gebze, Kocaeli, Turkey.
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