Development and Validation of a Predictive AI Framework for Diabetic Foot Ulcer Monitoring and Severity Assessment: A Step towards Self-monitoring and Primary Care Integration

다국어 초록 (Multilingual Abstract)

Objectives: Diabetic foot ulcer (DFU) is a critical complication of diabetes that can lead to severe outcomes such as infection,amputation, and increased mortality if left untreated. Early detection and continuous monitoring are essential but remainchallenging, especially in resource-limited settings such as India. This study developed and validated a deep learning algorithmto classify diabetic foot images into severity grades based on the International Working Group on the Diabetic Footclassification: grade 0 (healthy), grade 1 (mild), grade 2 (moderate), and grade 3 (severe). Methods: A dataset of 407 clinicalimages was collected from open-source platforms and clinics in South India and expanded to 612 images through dataaugmentation. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. Multiple machinelearning models were tested, including MobileNet_V2, EfficientNet-b0, DenseNet121, ResNet_50, VGG16, and ViT_b_16.
Results: Among the evaluated models, MobileNet_V2 demonstrated the highest validation accuracy (82%) and achieved anF1-score of 79% on the test set. Although the model showed strong training accuracy, minor overfitting was observed, particularlyin distinguishing adjacent severity grades. To address this, dropout, batch normalization, and early stopping wereemployed. Overall, the model generalized well, showing high accuracy in detecting healthy cases and acceptable performanceacross ulcer severity grades. Conclusions: This study underscores the potential of machine learning-based tools to supportfrontline healthcare workers and facilitate patient self-monitoring in low-resource environments. Future work will focus onrefining the model and integrating it into user-friendly applications.