Development and Multicenter External Validation of a Real-Time Artificial Intelligence Diagnostic System for Diabetic Peripheral Neuropathy Based on Wearable Devices

Diabetic peripheral neuropathy (DPN) ranks among the most common complications of diabetes worldwide, often leading to severe morbidity if undetected early. Traditional screening methods are time-intensive, require specialized personnel, and can be influenced by socioeconomic factors. This study aimed to develop and validate STRIDE (Sensor-based Tracking and Real-time Inference for Diabetic Peripheral Neuropathy Evaluation), an interpretable machine learning model utilizing gait and balance parameters from wearable devices to classify current DPN status. In this multicenter diagnostic study, we enrolled 206 participants from Chongqing hospitals, categorized as healthy controls (n=32), diabetics without DPN (n=47), asymptomatic DPN (n=48), and symptomatic DPN (n=79), with an independent cohort of 42 for external validation. Using LEG-Sys/BalanSens sensors, 68 features were extracted during one-minute walking and modified Clinical Test of Sensory Integration and Balance. The STRIDE model integrated random forest and logistic regression algorithms, demonstrating strong classification capability (random forest AUROC=0.80, 95% CI: 0.64–0.92; logistic regression AUROC=0.79, 95% CI: 0.62–0.95), with performance assessed via AUROC, accuracy, sensitivity, specificity, F1-score, and SHapley Additive exPlanations (SHAP) for interpretability. Key features identified included stride length variability (SHAP weight=0.61) and double-support time. External validation showed 89.3% concordance with electromyography, with three initial false positives developing DPN within one year. Additionally, STRIDE's real-time scoring demonstrates significant potential for scalable, resource-efficient DPN classification in diverse clinical and non-clinical settings, facilitating early intervention based on current biomechanical signatures.