Adapting Prompt-Driven Approach for Diabetic Foot Ulcer Segmentation: A Sri Lankan Patient Study

Diabetes is a common metabolic disorder affecting millions worldwide, including a significant population in Sri Lanka, and often leads to diabetic foot ulcers (DFUs). Accurate wound measurement is essential for diagnosing and monitoring DFUs, as it provides crucial metrics for tracking healing and guiding treatment decisions. However, manual measurement is time-consuming and prone to errors, potentially resulting in misdiagnoses and flawed records. Automating wound segmentation from medical images offers a promising solution, improving efficiency and patient outcomes. This study presents a prompt-driven segmentation approach to address the challenges of DFU segmentation, particularly in scenarios with limited data from Sri Lankan patients. By using user-provided bounding boxes, the model focuses on complex wound areas, improving accuracy in cases with unclear boundaries or sparse data. The research involves creating a diverse dataset of 300 samples from 180 patients at Dambulla Base Hospital, capturing variations in skin tone, ulcer size, shape, and surrounding tissue conditions. The proposed approach fine-tunes the Segment Anything Model (SAM) using prompt-driven techniques to adapt to the unique characteristics of DFUs in Sri Lanka. The model achieved strong performance metrics, including a Dice Coefficient of 83.31%, Specificity of 99.73%, and Precision of 90.42%. This method reduces the need for large annotated datasets, enhances gener-alization from limited data, and allows user-guided adjustments. By developing population-specific models, this research has the potential to improve clinical decision-making and significantly enhance DFU management in Sri Lanka. Furthermore, this approach demonstrates the broader applicability of prompt-driven segmentation techniques to address healthcare challenges in resource-constrained settings.