Clinical biomechanics (Bristol, Avon) · 2026
A comprehensive biomechanical phenotyping framework for diabetic foot ulcer risk stratification using multi-modal gait analysis and machine learning.
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Jafarzadeh Mohsen, Golpaygani Ali Tavakoli, Ghomshe Farhad Tabatabaei
Аннотация
BACKGROUND: Current diabetic foot ulcer risk assessment methods lack precision in identifying high-risk biomechanical phenotypes. This study aimed to develop a comprehensive biomechanical profiling framework integrating multi-modal gait analysis with machine learning for enhanced ulcer risk stratification.
METHODS: In this prospective cross-sectional study, we analyzed214 participants: active diabetic foot ulcer patients (n = 68), diabetic controls without ulceration (n = 73), and healthy controls (n = 73). We implemented a multi-modal assessment protocol combining high-resolution plantar pressure mapping, wearable inertial sensors, 3D motion capture, and electromyography. Machine learning algorithms included unsupervised learning for phenotyping and supervised learning for predictive modeling, validated through nested cross-validation.
FINDINGS: Diabetic foot ulcer patients demonstrated significantly elevated forefoot pressures (metatarsal 1: 21.3 ± 4.8 vs 15.2 ± 3.1 N/cm2, p < 0.001), altered pressure-time integrals, and cautious gait patterns (velocity: 1.12 ± 0.14 vs 1.45 ± 0.16 m/s, p < 0.001). K-means clustering revealed four distinct biomechanical phenotypes with differential ulceration risks (OR: 3.2-8.7). The random forest model achieved 94.3% accuracy (95% CI: 91.2-96.8%) in classifying diabetic foot ulcer risk using six key biomechanical features, substantially outperforming conventional methods. Dynamic center of pressure analysis identified previously unrecognized instability patterns predictive of ulcer development 6-8 months before clinical presentation.
INTERPRETATION: We identified and validated novel biomechanical phenotypes with differential ulcer susceptibility. The integration of machine learning with multi-modal gait analysis enables precise risk stratification and personalized prevention strategies, representing a paradigm shift from reactive treatment to proactive, phenotype-specific diabetic foot care.
METHODS: In this prospective cross-sectional study, we analyzed214 participants: active diabetic foot ulcer patients (n = 68), diabetic controls without ulceration (n = 73), and healthy controls (n = 73). We implemented a multi-modal assessment protocol combining high-resolution plantar pressure mapping, wearable inertial sensors, 3D motion capture, and electromyography. Machine learning algorithms included unsupervised learning for phenotyping and supervised learning for predictive modeling, validated through nested cross-validation.
FINDINGS: Diabetic foot ulcer patients demonstrated significantly elevated forefoot pressures (metatarsal 1: 21.3 ± 4.8 vs 15.2 ± 3.1 N/cm2, p < 0.001), altered pressure-time integrals, and cautious gait patterns (velocity: 1.12 ± 0.14 vs 1.45 ± 0.16 m/s, p < 0.001). K-means clustering revealed four distinct biomechanical phenotypes with differential ulceration risks (OR: 3.2-8.7). The random forest model achieved 94.3% accuracy (95% CI: 91.2-96.8%) in classifying diabetic foot ulcer risk using six key biomechanical features, substantially outperforming conventional methods. Dynamic center of pressure analysis identified previously unrecognized instability patterns predictive of ulcer development 6-8 months before clinical presentation.
INTERPRETATION: We identified and validated novel biomechanical phenotypes with differential ulcer susceptibility. The integration of machine learning with multi-modal gait analysis enables precise risk stratification and personalized prevention strategies, representing a paradigm shift from reactive treatment to proactive, phenotype-specific diabetic foot care.