Unraveling Chronic Ankle Instability: A Data-Driven Clustering Approach to Redefine Subtypes and Improve Diagnosis

Individual differences in the biomechanical characteristics of chronic ankle instability (CAI) and the heterogeneity in treatment responses suggest that CAI may have distinguishable subtypes. However, the existing selection criteria for CAI are limited, and the current CAI model groups various types of ankle instability without any precise differentiation of subtypes. This study aimed to apply clustering analysis to identify distinct CAI subtypes. An ordered dataset representing three CAI types (perceived ankle instability (PAI), functional ankle instability (FAI), and mechanical ankle instability (MAI)) was designed, and the K-means clustering algorithm was then applied to clinical data from 210 participants, including individuals with CAI, copers, and healthy people. Clustering analysis was performed using the Cumberland Ankle Instability Tool (CAIT), Identification of Functional Ankle Instability (IdFAI), and anterior drawer test (ADT) scores as indicators, followed by dimensionality reduction and cluster validation. The K-Means clustering algorithm identified five distinct CAI subtypes: PAI, FAI, PAI+FAI, PAI+FAI+MAI, and Sub-coper. The clustering model based on clinical data confirmed the absence of pure MAI and showed that CAI patients could present with varying levels of instability. The most prevalent subtype might be a combination of PAI and FAI. This study demonstrates that, by using clustering analysis, CAI can be categorized into distinct subtypes, offering a more precise diagnostic framework. This approach supports the development of subgroup-based management strategies for CAI and highlights the need for updated selection criteria for CAI.