Combining thermography and artificial intelligence in comparison with a diabetic foot nurse for diabetic foot ulcer detection: A diagnostic accuracy study

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.