Abstract
Diabetic foot complications, caused by prolonged hyperglycemia, are a significant health concern among diabetes patients. Majority of patients develop diabetic foot complications, contributing significantly to diabetes-related hospital admissions. These complications include foot ulcers, infections, ischemia, Charcot foot, and neuropathy. They also increase the risk of amputation, affecting quality of life and putting strain on healthcare systems. At this stage, early diagnosis plays a vital role. The process of diagnosing involves not only identifying the presence or absence of a disease, but also categorizing the disease. In this study, we examine the use of deep learning methods in the diagnosis of diabetic foot conditions. It explores various aspects such as predictive modeling and image analysis. The study discusses the progression of model designs, data sources, and interpretability methodologies, with a focus on improving accuracy and early detection. Overall, the study provides a comprehensive analysis of the current state of deep learning in diabetic foot problems with highlighting advancements.
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