Sub135 SYSTEMATIC REVIEW ON THE ROLE OF ARTIFICIAL INTELLIGENCE IN DIAGNOSING AND MANAGING MUSCULOSKELETAL DISORDERS AND INJURIES
DOI:
https://doi.org/10.71000/v077b679Keywords:
Artificial Intelligence, Musculoskeletal Diseases, Systematic Review, Diagnostic Accuracy, Learning, Orthopedics.Abstract
Background: Musculoskeletal disorders represent a leading cause of global disability, creating a pressing need for innovations in diagnostic and management strategies. Artificial intelligence (AI) has emerged as a transformative tool with potential applications in interpreting medical images and predicting patient outcomes for these conditions. However, the evidence regarding its efficacy remains fragmented across various applications, necessitating a comprehensive synthesis.
Objective: This systematic review aims to evaluate the current evidence on the performance of AI applications in diagnosing and managing a broad spectrum of musculoskeletal disorders and injuries, comparing its accuracy to standard clinical or radiological assessments.
Methods: A systematic literature search was conducted in accordance with PRISMA guidelines across PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library for studies published between January 2014 and June 2024. Inclusion criteria encompassed original studies evaluating AI models for musculoskeletal conditions against a reference standard. Two independent reviewers performed study selection, data extraction, and risk of bias assessment using appropriate tools like QUADAS-2. A qualitative synthesis was undertaken due to heterogeneity.
Results: Eight studies involving 21,450 patients and image series were included. AI models, primarily deep learning networks, demonstrated high performance in detecting fractures (AUC up to 0.97, sensitivity up to 98.5%), grading osteoarthritis (accuracy up to 88%), and assessing soft-tissue pathologies like meniscal and rotator cuff tears (sensitivity 94%, correlation r=0.91). Performance was often statistically significant (p<0.001) and comparable to expert clinicians. Common limitations in the included studies were retrospective design and potential for verification bias.
Conclusion: AI models show significant promise as accurate tools for assisting in the diagnosis and quantification of musculoskeletal conditions, performing on par with clinical experts in controlled research settings. These findings support their potential role as decision-support tools in clinical workflows. Future research should prioritize prospective, real-world validation and focus on evaluating the impact of AI integration on long-term patient outcomes and clinical efficiency.
References
Cieza A, Causey K, Kamenov K, Hanson SW, Chatterji S, Vos T. Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2021;396(10267):2006-2017.
Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach. Sci Rep. 2018;8(1):1727.
Gan K, Xu D, Lin Y, et al. Artificial Intelligence Detection of Distal Radius Fractures: A Comparison Between the Convolutional Neural Network and Professional Assessments. Acta Orthop. 2021;92(6):699-704.
Pedoia V, Norman B, Mehany SN, Bucknor M, Link T, Majumdar S. 3D Convolutional Neural Networks for Detection and Severity Staging of Meniscus Root Tears and Horizontal Cleavage Tears. Radiology. 2021;299(1):177-185.
Grotepass C, Vetter SY, Macke C, et al. Deep Learning for Automated and Markerless Assessment of Rotator Cuff Tear Size Using External Rotation View Shoulder Radiographs. J Shoulder Elbow Surg. 2023;32(10):2100-2109.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
Babineau J. Product review: Covidence (systematic review software). Journal of the Canadian Health Libraries Association/Journal de l'Association des bibliothèques de la santé du Canada. 2014 Aug 1;35(2):68-71.
Bien N, Rajpurkar P, Ball RL, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development of a robust tool for detecting internal derangement. J Magn Reson Imaging. 2024;59(1):300-310.
Kim KC, Lee HJ, Lee SH, et al. Development and Validation of a Deep Learning Algorithm for Shoulder Impingement Syndrome Using Shoulder Ultrasonography. J Digit Imaging. 2023;36(2):567-576.
vheehan SE, Sohn JH, Liu F, et al. Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis. J Arthroplasty. 2024;39(2):456-464.
Sheehan SE, Geis JR, Hemal K, et al. Assessing the Performance of an Artificial Intelligence Tool for the Detection of Cervical Spine Fractures. AJNR Am J Neuroradiol. 2024;45(2):224-229.
Higgins JP, Sterne JA, Savovic J, Page MJ, Hróbjartsson A, Boutron I, Reeves B, Eldridge S. A revised tool for assessing risk of bias in randomized trials. Cochrane database of systematic reviews. 2016;10(Suppl 1):29-31.
Guni A, Sounderajah V, Whiting P, Bossuyt P, Darzi A, Ashrafian H. Revised tool for the quality assessment of diagnostic accuracy studies using AI (QUADAS-AI): protocol for a qualitative study. JMIR Research Protocols. 2024 Sep 18;13(1):e58202.
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Copyright (c) 2025 Anila Zanib, Fatima Riaz , Shazia Nazar, Erum Afaq, Zainab Askari , Sadaf Moeez (Author)
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