{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:50:34Z","timestamp":1782233434775,"version":"3.54.5"},"reference-count":39,"publisher":"S. Karger AG","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"},{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/karger.com\/pages\/terms-and-conditions"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Gerontology"],"abstract":"<jats:p>Introduction: Diabetic peripheral neuropathy (DPN) is a frequent diabetes complication, affecting over half of patients and causing pain, falls, ulcers, and amputations if undetected. Traditional methods like electromyography are resource-intensive and inaccessible. This study developed and validated STRIDE, a machine learning model using wearable gait and balance data for real-time DPN classification. Methods: In this study conducted across multiple centers, 206 participants from Chongqing hospitals were categorized as follows: healthy controls (n = 32), diabetics without DPN (n = 47), asymptomatic DPN (n = 48), and symptomatic DPN (n = 79). A separate group of 42 was used for validation. During a one-minute walk and modified balance test, LEG-Sys\/BalanSens sensors produced 68 features. STRIDE used random forest and logistic regression with cross-validation. The metrics used were area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, F1-score, and SHAP for interpretability, with a scoring system allowing for real-time evaluation. Results: Random forest achieved AUROC 0.80 (95% CI: 0.64\u20130.92); logistic regression 0.79 (95% CI: 0.62\u20130.95). Key SHAP features: stride length variability (weight = 0.61) and double-support time. External validation: initial accuracy 78.6%, recall 86.5%, precision 88.9%; three of four false positives developed DPN within 1 year, yielding 89.3% adjusted concordance. STRIDE outperformed other parameters in mid-term prediction, unaffected by anemia for up to 4 years. Conclusion: STRIDE provides a scalable, interpretable tool for DPN classification, supporting early intervention and improved outcomes. Larger longitudinal studies are needed for clinical integration.<\/jats:p>","DOI":"10.1159\/000551704","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:01:04Z","timestamp":1775664064000},"page":"1-17","source":"Crossref","is-referenced-by-count":0,"title":["Development and Multicenter External Validation of a Real-Time Artificial Intelligence Diagnostic System for Diabetic Peripheral Neuropathy Based on Wearable 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