Key result
Machine learning models predict sports musculoskeletal injury risk but lack external validation across athlete populations.
Why the study?
Sports-related musculoskeletal injuries are common with significant physical and economic consequences, and machine learning offers a method for early prediction and prevention.
Do machine learning models predict sports-related musculoskeletal injuries in athletes?
Do machine learning models predict sports-related musculoskeletal injuries in athletes?
Machine learning models show promise in predicting sports-related musculoskeletal injuries using multimodal data, but require prospective validation and improved transparency before clinical implementation.
Cautions against clinical adoption without validation; leaves generalizability across athletes open for prospective research.
Plain Language Summary:Sports injuries often occur unexpectedly and can significantly affect athletic performance and long-term health. Machine learning can analyze wearable sensor data, medical imaging, and performance metrics to predict injury risk. This review evaluates current research, challenges, and future directions. Abstract:Background: Sports-related musculoskeletal injuries are common and can have significant physical and economic consequences. Machine learning offers a method for early injury prediction and prevention. Objective: To evaluate current applications, performance, limitations, and future directions of machine learning models for predicting sports-related musculoskeletal injuries. Methods: A structured literature review of peer-reviewed studies published between 2020 and 2026 was conducted. Results: Machine learning models, including Random Forest, CNNs, and RNNs, demonstrated the ability to predict injury risk using wearable sensors, imaging, and athlete metrics. Many models lacked external validation and generalizability. Conclusion: Machine learning has strong potential for injury prediction and prevention but requires further validation and ethical implementation before clinical use.
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Muhammad Ebadur Rahman Siddiqui (2026) studied Athletes of various sports at risk for musculoskeletal injuries (n=16). Machine learning-based injury prediction models (Random Forest, convolutional neural networks, recurrent neural networks) vs. Standard clinical injury risk assessment or none was evaluated on Prediction accuracy of sports-related musculoskeletal injury risk. Machine learning models demonstrated the ability to predict sports-related musculoskeletal injury risk but showed limited external validation and generalizability across athlete populations.
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