Background Early detection of diabetic foot complications is essential to prevent ulcers and amputations. Thermographic imaging offers a non-invasive method for identifying risk, but clinical interpretation has traditionally relied on human thermographers. Artificial intelligence (AI) may offer a more scalable and objective alternative. Objective To evaluate the diagnostic performance of an AI-powered thermographic screening tool in identifying risk for diabetic foot complications, compared to nurse-led clinical assessment. Methods We conducted a cross-sectional study of 100 adults with diabetes undergoing routine foot screening. For each participant, a smartphone-based thermal imaging device was first used to capture plantar images, from which the AI model generated risk scores (0–3). Second, a diabetic foot nurse performed a clinical examination and assigned the reference risk scores (0–3). Absolute temperature differences were computed from thermal images, and diagnostic accuracy metrics were calculated using the nurse assessment as the reference standard. Results The AI system demonstrated 100% sensitivity, 96.8% specificity, 66.7% positive predictive value, and 100% negative predictive value for detecting moderate-to-high risk cases. There was a strong correlation between AI and nurse scores (ρ = 0.973), and both assessors showed increasing temperature asymmetry with higher risk levels. Conclusions The AI model accurately detected all moderate-to-high risk cases flagged by the nurse, with high sensitivity and specificity. Its strong alignment with thermal data and consistent scoring suggest its value as a scalable and reproducible adjunct for diabetic foot screening. Further validation in longitudinal settings may support broader integration in remote and primary care environments.
Shara et al. (Thu,) studied this question.
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