Automated detection of inflammation in foot thermographic images of patients with diabetes acquired in an uncontrolled clinical environment.

AIMS: Quantitative methods for detecting diabetic foot inflammation remain limited, though localized skin-temperature elevation is a promising early indicator. Because background variability can hinder AI-based foot identification, we evaluated AI-driven thermal imaging for diabetic foot inflammation under uncontrolled clinical backgrounds and assessed whether curtain-based background control and grayscale conversion improved detection.

METHODS

Cross-sectional study design was conducted in Makassar, Indonesia, 100 plantar thermal images from 50 diabetic patients were acquired with and without a curtain, preprocessed (sensitivity, 1.5; emissivity, 0.98), and converted to grayscale. Expert-identified inflammatory hotspots were annotated and used to train a YOLO11n model with 5-fold cross-validation, evaluated using accuracy, precision, sensitivity, specificity, ROC-AUC, IoU, and case-error analysis.

RESULTS

With a curtain, color-map images achieved sensitivity of 0.773, versus 0.455 without one. Grayscale images performed consistently across conditions, with best results in the mixed dataset (accuracy, 0.980; precision, 0.976; sensitivity, 0.932; specificity, 0.994; AUC 0.966 vs. 0.886 for color-map). Correctly detected cases showed IoU > 0.9. Low thermal contrast and small region of interest were the most frequent failure categories.

CONCLUSIONS

AI-driven thermal imaging detects diabetic foot inflammation under uncontrolled conditions. A curtain improved color-map detection, but grayscale imaging provided robust, curtain-independent performance, supporting its use for clinical screening.