## 1 Introduction  
Diabetes, a widespread disease affecting approximately 537 million individuals, is projected to rise to 783 million by 2045, according to research by the International Diabetes Federation (IDF) [1]. Diabetic foot ulcer (DFU) is a prevalent complication among individuals with diabetes mellitus. Preventing and effectively treating diabetic foot ulcers is a challenging task due to their high recurrence rate. The presence of a DFU has been shown to significantly impact health-related quality of life, resulting in decreased physical function and mobility [2]. This condition increases the risk of lower limb amputations in individuals with diabetes. When treating diabetic foot ulcers, promptness and assertiveness can make a significant difference in slowing the wound's progression and preventing the need for amputation [3]. Figure 1 demonstrate the rapid progression of a DFU over ten days. Patients with diabetic foot ulcers (DFUs) and uncontrolled diabetes often experience a significant [5] decrease in their quality of life. This is closely linked to frequent hospitalizations and a notable increase in treatment costs, particularly when the DFU fails to heal or recurs. In addition to the crucial need for early detection interventions, the integration of technology offers a promising avenue for enhancing the accuracy of results and simplifying examination procedures. By exploring technological solutions, we can potentially achieve more accurate diagnoses and streamline the overall assessment process. In Mauritius, the diabetic population currently stands at 22.6%, and this figure is projected to increase to 26.6% by the year 2045 [1]. Furthermore, in 2021, the rate of pre-diabetic individuals reached 15.9% [6], adding to the existing 22.6% prevalence. According to the Foot Care Clinic of APSA International, located in Mauritius, approximately 500 individuals undergo amputations each year as a result of type 2 diabetes, with an estimated 88% of these cases being preventable [7]. Annually, an estimated 67% of amputations in the United States and 90% of amputations in the United Kingdom are attributed to diabetes [8]. In healthcare, machine learning (ML) applications are predominantly employed to analyze extensive datasets, helping doctors and other medical professionals in making informed decisions. This technology proves invaluable in identifying anomalies, patterns, and trends, thus reducing the potential for human error and enhancing diagnostic accuracy. Research has shown that AI can accurately diagnose conditions such as retinal disorders [9], diabetic retinopathy [10], breast cancer [11], and skin cancer [12] through digital images. Furthermore, [PERSON] et al. [13] showed that a deep learning classification algorithm on images of retinopathy had comparable or better accuracy than clinical experts. When addressing the prevention and treatment of diabetic foot ulcers (DFUs), the adoption of a multidisciplinary approach becomes crucial [14]. In their study, [PERSON] et al. [15] investigated the reliability of utilizing mobile images for remote DFU assessment. Unfortunately, their experiment did not yield conclusive results, leading them to caution against  
Figure 1: DFU images for Use-Case #3 [4]. relying solely on mobile images for making treatment decisions due to low validity and reliability. They recommended that clinicians gather as much additional information as possible when utilizing such images. In contrast, the system we propose maintains the importance of clinical expertise while leveraging the power of deep learning to provide more information for evaluating medical images. This additional information aims to enhance the clinician's ability to design personalized treatment protocols for individual patients. By combining the strengths of deep learning and clinical expertise, our system seeks to bridge the gap and provide valuable support to clinicians in making informed decisions regarding DFU treatment.