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

Abstract

Background:

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.

Methods:

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.

Results:

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.

Conclusions:

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