Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.

Objective: This study aims to conduct an in-depth analysis of diabetic foot ulcer (DFU) images using deep learning models, achieving automated segmentation and classification of the wounds, with the goal of exploring the application of artificial intelligence in the field of diabetic foot care. Methods: A total of 671 images of DFU were selected for manual annotation of the periwound erythema, ulcer boundaries, and various components within the wounds (granulation tissue, necrotic tissue, tendons, bone tissue, and gangrene). Three instance segmentation models (Mask2former, Deeplabv3plus, and Swin-Transformer) were constructed to identify DFU, and the segmentation and classification results of the three models were compared. Results: Among the three models, Mask2former exhibited the best recognition performance, with a mean Intersection over Union of 65%, surpassing Deeplabv3's 62% and Swin-Transformer's 52%. The Intersection over Union value of Mask2former for wound recognition reached 85.9%, with IoU values of 80%, 78%, 62%, 61%, 47%, and 39% for granulation tissue, gangrene, bone tissue, necrotic tissue, tendons, and periwound erythema, respectively. In the wound classification task, the Mask2former model achieved an accuracy of 0.9185 and an Area Under the Curve of 0.9429 for the classification of Wagner grade 1-2, grade 3, and grade 4 wounds. Conclusion: Among the three deep learning models, the Mask2former model demonstrated the best overall performance. This method can effectively assist clinicians in recognizing DFU and segmenting the tissues within the wounds.