AI-driven medical image analysis for sports injury diagnosis and prevention

Abstract

Sports-related injuries present significant challenges in diagnosis, prevention, and rehabilitation, often requiring precise assessments across multiple imaging modalities. Traditional diagnostic approaches rely on manual interpretation, which is time-consuming and prone to variability. To address these limitations, we propose an AI-driven framework integrating deep learning, biomechanical modeling, and adaptive decision-making for injury prediction and rehabilitation optimization. The Biomechanically-Informed Neural Network (BINN) fuses kinematic, physiological, and performance data using attention mechanisms, enhancing both interpretability and predictive accuracy. BINN processes motion capture data through convolutional and recurrent layers, extracting meaningful biomechanical patterns to assess movement efficiency and injury risk. The Adaptive Sports Medicine Strategy (ASMS) dynamically adjusts injury risk predictions and rehabilitation strategies in real-time by continuously integrating new physiological and biomechanical data. By leveraging self-attention and multimodal data fusion, ASMS optimizes athlete monitoring and intervention planning. Experimental results across multiple datasets, including CamVid, MSRA10K, DUT-OMRON, and NYU Depth V2. The proposed framework not only enhances injury risk assessment but also provides personalized rehabilitation recommendations, ensuring optimal recovery and performance. This study highlights the potential of AI-driven sports medicine, paving the way for more accurate, interpretable, and responsive injury management systems.

Ähnliche Arbeiten