Title: Machine learning approaches for early detection and management of musculoskeletal conditions

Authors: Pawan Whig; Ebtesam Shadadi; Shama Kouser; Lathifah Alamer

Addresses: Vivekananda Institute of Professional Studies-Technical Campus, AU, Block Pitam Pura Delhi-34, India ' Jazan University, Al Maarefah Rd., Jazan 45142, Saudi Arabia ' Jazan University, Al Maarefah Rd., Jazan 45142, Saudi Arabia ' Jazan University, Al Maarefah Rd., Jazan 45142, Saudi Arabia

Abstract: Musculoskeletal conditions have a significant impact on quality of life. This study explores the use of machine learning algorithms for early detection and management of such conditions. Different models were evaluated using a dataset of musculoskeletal images and clinical information. Results demonstrate accurate classification with high sensitivity and specificity. A neural network was developed for detecting chronic lower back pain, achieving an impressive validation F1 score of 89%-93%. This highlights the potential of artificial intelligence in improving early detection and management. Future research should address data outliers to enhance model performance. Overall, neural networks are a valuable tool for early detection and management of musculoskeletal conditions, leading to improved patient outcomes. These findings suggest promising avenues for future research and implications for early detection and management in this field.

Keywords: musculoskeletal conditions; arthritis; fractures; spinal problems; machine learning; early detection.

DOI: 10.1504/IJCVR.2025.142916

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.1, pp.104 - 117

Received: 04 Feb 2023
Accepted: 15 Mar 2023
Published online: 02 Dec 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article