Advanced AI-Driven Thermographic Analysis for Diagnosing Diabetic Peripheral Neuropathy and Peripheral Arterial Disease

Abstract

This study explores the integration of advanced artificial intelligence (AI) techniques with infrared thermography for diagnosing diabetic peripheral neuropathy (DPN) and peripheral arterial disease (PAD). Diabetes-related foot complications, including DPN and PAD, are leading causes of morbidity and disability worldwide. Traditional diagnostic methods, such as the monofilament test for DPN and ankle–brachial pressure index for PAD, have limitations in sensitivity, highlighting the need for improved solutions. Thermographic imaging, a non-invasive, cost-effective, and reliable tool, captures temperature distributions of the patient plantar surface, enabling the detection of physiological changes linked to these conditions. This study collected thermographic data from diabetic patients and employed convolutional neural networks (CNNs) and vision transformers (ViTs) to classify individuals as healthy or affected by DPN or PAD (not healthy). These neural networks demonstrated superior diagnostic performance, compared to traditional methods (an accuracy of 95.00%, a sensitivity of 100.00%, and a specificity of 90% in the case of the ResNet-50 network). The results underscored the potential of combining thermography with AI to provide scalable, accurate, and patient-friendly diagnostics for diabetic foot care. Future work should focus on expanding datasets and integrating explainability techniques to enhance clinical trust and adoption.