AI driven detection and remediation of Diabetic Foot Ulcer (DFU)

Abstract

Diabetic foot ulcers (DFUs) constitute a significant health concern in India, affecting a substantial portion of diabetic patients. Without prompt intervention, these ulcers can result in severe complications, including infection, gangrene, amputation, and chronic wounds. Approximately 72% of DFU patients test positive for multidrug-resistant organisms (MDROs), further elevating the risk of complications. Early detection is critical to preventing such outcomes. This prototype leverages artificial intelligence (AI) and deep learning techniques, specifically Convolutional Neural Networks (CNNs), for the detection and assessment of DFUs. By analyzing annotated medical images, the system accurately measures the size and depth of ulcers using CNNs. AI enables early diagnosis, facilitating timely and customized treatments, enhancing clinical decision-making, and mitigating the risks associated with advanced DFUs. The system employs an ESP32 camera to capture real-time images of the ulcers. Following image capture, the CNN algorithm performs image masking to isolate the ulcer region. The wound's contours are displayed on a terminal, and the severity percentage of the ulcer is calculated, along with recommended interventions based on the wound's stage. This approach not only reduces healthcare costs but also improves patient outcomes by preventing severe complications. The study underscores the importance of early diagnosis and highlights AI's potential in the effective management of DFUs.