Machine Learning Sports Injury Prediction: A Structured Review

Abstract

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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