Multiscale Theoretical Foundations of DiabeticNeuropathy and AI-Driven Diagnosis

This study establishes an integrated theoretical framework for understanding and diagnosing diabetic neuropathy through combined metabolic, electrophysiological, and artificial intelligence (AI) approaches. We derive mathematical models characterizing polyol pathway kinetics (kdep = 0.18 month−1), sodium channel decay (τ = 8.2 months), and conduction velocity reduction (Δθ = 28.4%). The machine learning (ML) implementation achieves 92.5% diagnostic accuracy with optimal decision threshold at θ∗ = 0.63. Our analysis reveals three critical pathophysiological phases: initial NADPH depletion exceeding 0.4 mM triggers oxidative stress, followed by progressive ion channel dysfunction, and ultimately leads to measurable conduction deficits. The framework bridges molecular mechanisms to clinical manifestations, demonstrating a strong correlation between metabolic markers (NADPH, AGEs) and electrophysiological parameters (conduction velocity, propagation failure). Validation against clinical datasets confirms model robustness across disease stages, with the staging system showing 89% concordance with expert assessments(κ = 0.81). Our SVM-CNN hybrid model demonstrates superior performance (AUC=0.94, ΔAUC=+0.12 vs. conventional methods), enabling detection 6 months earlier than current standards. SHAP analysis identifies NADPH depletion rate (importance weight=0.41) as the top predictive biomarker. These results provide quantitative biomarkers for early detection and a foundation for AI-enhanced personalized management of diabetic neuropathy.