AI-Based Early Detection of Diabetic Foot Ulcers

Abstract

Diabetic foot ulcers (DFUs) are a global cause of lower-limb amputation and a major factor in severe outcomes due to delayed diagnosis. Traditional detection is based on visual and tactile inspection, frequently failing to detect subclinical temperature differences that are harbingers of obvious overt skin breakdown. This article suggests an AI-based, smartphone-compatible thermal imaging camera for employment in the early detection of DFUs to facilitate early treatment and avoid amputation. The introduced framework incorporates low-cost thermal imaging add-ons, heat anomaly detection using convolutional neural networks (CNNs), and cloud-based telemedicine services. The model takes advantage of temperature asymmetry analysis between the feet to detect pre-ulcerative inflammation. Deployment in primary care and home-monitoring settings is described, along with potential pitfalls, ethical considerations, and mitigation strategies. The anticipated outcome is a scalable, low-cost diagnostic device that empowers patients, assists clinicians, and fundamentally enhances diabetic foot care outcomes.