From Biomechanical Markers to Risk Prediction: Machine Learning Advances in Early Warning of Recurrent Acute Ankle Sprain

Vollständiger Abstract

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Acute lateral ankle sprain is among the most frequent injuries in sports medicine, and its high recurrence rate and propensity toward chronic ankle instability (CAI) constitute a persistent clinical challenge. Conventional risk assessment—relying on subjective questionnaires, physical examination, and clinical experience—captures only part of the complex biomechanical and neuromuscular adaptations that follow an initial sprain. Recent advances in objective biomechanical profiling and machine learning (ML) have opened a new paradigm for individualized recurrence prediction. This review synthesizes the full translational chain from biomechanical marker identification to ML-based risk prediction. We first summarize key markers spanning gait kinetics (ground reaction forces and joint moments), proprioceptive and neuromuscular control deficits, and dynamic postural stability, highlighting the representational advantages of multimodal data fusion. We then compare mainstream ML architectures—including tree-based ensembles, recurrent networks for gait time series, and strategies for small-sample learning—and discuss the role of explainable artificial intelligence (XAI) in linking predictions to injury mechanisms. Evidence indicates that models integrating multimodal biomechanical features outperform conventional clinical scores (e.g., Ankle-GO, AUC 0.70), with few-shot learning systems achieving test accuracies of 0.89 and inertial-sensor-driven recurrent networks estimating ankle kinematics with coefficients of determination up to 0.93. Finally, we evaluate validation strategies, clinical utility in rehabilitation prescription and return-to-sport decisions, and wearable-based long-term monitoring, and we dissect the outstanding challenges of data standardization, model interpretability, annotation scarcity, and ethical governance. Emerging technologies—digital twins, federated learning, and generative AI for data augmentation—offer plausible routes toward a precise, interpretable, and equitable intelligent prevention ecosystem for recurrent ankle sprain.