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Title |
Advances in Musculoskeletal Disorder Diagnosis: A Comprehensive Review of Recent Deep Learning-Based Approaches |
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Author |
Qamar Uddin Khand, Sajid Khan, Adnan Manzoor, Muhammad Asif Khan, Muhammad Bilal Qureshi |
| Citation |
Vol. 25 No. 7 pp. 43-52 |
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Abstract |
Musculoskeletal disorders (MSDs) affect muscles, bones, tendons, joints, and nerves, often causing significant pain and limiting movement. They typically result from repetitive strain, poor posture, or workplace hazards. Early diagnosis and effective treatment are essential for improving quality of life. MSDs are classified by type, severity, and affected body parts, making accurate classification vital for targeted therapy. Traditional diagnosis relies on expert evaluation, which can be slow and labor-intensive. To address this, automated detection and classification systems are being developed to enhance diagnostic accuracy and efficiency. Advances in imaging technologies such as radiographs, MRI, and ultrasound, combined with deep learning (DL) methods, show great potential in identifying and classifying MSDs. This review examines the classification, localization, and segmentation of these conditions, highlighting key datasets, imaging techniques, and DL applications. The goal is to consolidate developments from 2020 to 2024 within DL frameworks. While deep learning presents promising solutions for MSD diagnosis, challenges like data availability, interpretability, and generalization remain areas for further research. |
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Keywords |
Classification, Computer-Aided Diagnosis (CAD), Deep Learning, Musculoskeletal Disorders (MSDs), Medical Imaging, Segmentation. |
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