AI-Enhanced Imaging for Diabetic Foot Ulcer Risk Assessment and Diagnosis: A Retrospective Cohort Study

The automated assessment and prediction of diabetic foot ulcer (DFU) severity depends heavily on precise segmentation of the ulcer region. This approach avoided reliance on built-in segmentation tools, which often lacked the accuracy needed to delineate wound boundaries effectively. The objective of this study was to develop and evaluate an artificial intelligence (AI)-driven method for ulcer segmentation and severity classification of DFU using Wagner's grading system.

A novel method was introduced for segmenting the boundaries of DFUs, paired with a lightweight classification model for predicting ulcer severity as per Wagner's grade. This method was developed using a retrospective cohort of patients in India. A total of 1339 ulcer images were collected from 510 patients and augmented to 6579 images for AI-model generalizability. It incorporated an enhanced active contour model, combined with Sobel edge detection, to achieve precise delineation of ulcer edges. An AI-powered mobile application was developed to facilitate the real-time and remote assessment of the severity of DFUs.

The proposed segmentation approach successfully delineated ulcer regions, achieving a Dice similarity coefficient of 0.99. The classification model attained an accuracy of 95.58%, with a sensitivity of 95.58%, a specificity of 99.16%, and an F1 score of 95.53%. The method also recorded a false-positive rate of 0.84% and a false negative rate of 4.83%, reflecting improved classification performance compared to existing methods.

The comparative analysis demonstrated that the proposed method significantly improved both segmentation and classification of DFUs, thereby supporting enhanced clinical management of the condition.