# Morphological Foot Model for Temperature Pattern Analysis Proposed for Diabetic Foot Disorders
## 1 Introduction  
Abnormal plantar temperature in patients with diabetes may be an early sign indicating the appearance of foot disorders which include peripheral arterial disease, neuropathy, and infection among others [1, 2]. These pathologies are often resistant to healing and require long-term medical care [3]. Preventive care by regular monitoring and correct assessment limits the incidence of disabling conditions and the lower-limb amputations associated with acute cases [4, 5, 6]. Therefore, improved medical care, by monitoring the patient's condition over time, is required to identify higher risk patients and prevent the significant impact caused by economic burden [7]. This demands fast, simple, autonomous, repeatable and precise screening protocols, avoiding error-prone and labor-intensive processes as well as the requirement of special training [6, 7]. Infrared thermography is a non-invasive tool that maps the surface temperature of a body [8]. This marker-free, safe, accessible, contactless, portable, and easily repeatable technique has been widely applied in vascular diseases, particularly diabetic footdisorders [2; 4; 9], since the temperature of extremities is largely dependent on peripheral blood flow vessels [8]. The appearance of low-cost devices, based on microbolometers, has favored the use of infrared cameras as supplementary diagnostic tools. These affordable devices allow clinical practitioners to measure the superficial temperature under controlled ambient environment [10] providing multiple recordings at short time intervals. The viability of infrared sensors to analyze anomalous temperature patterns in diabetic diseases has been previously reported [11; 12; 13]. Nevertheless, in patients with diabetes, the detection of inflammatory processes is hard to assess objectively when peripheral pathologies are present [14]. Foot temperature asymmetry has been employed to diagnose and monitor the severity of diabetic foot complications [15]. The assessment is based on the difference between the mean plantar temperature of the affected foot and unaffected foot. Basically, if this temperature difference surpasses a certain threshold, an alarming sign is set. However, the optimal cut-off is a subject of controversy. In patients affected by diabetic foot infection, a plantar foot asymmetry of 1.35 \({}^{\circ}\)C suffices to seek urgent treatment [16], whereas a threshold of 2.2 \({}^{\circ}\)C is usually considered for an inflammatory process and the impending development of a foot ulcer [7; 17; 18]. In fact, it has been suggested that thermal asymmetry monitoring has no additional value for assessing the severity of diabetic foot infections [15]. Moreover, for patients at a high risk of developing foot ulcers, monthly intervention by thermography did not result in a significant reduction in ulcer recurrence rate or increased ulcer-free survival [19]. On a side note, in certain cases, foot temperature asymmetry requires the exclusion of patients with partial amputations or deformities [7]. This is not optimal since these are common features exhibited by patients affected by the pathology at hand. Machine (ML) and Deep learning (DL) approaches have been employed for image segmentation, localization, classification, raw data analysis and risk assessment of chronic wounds, including diabetic foot ulcers (DFU) [7; 20; 21; 22]. ML and DL algorithms provide an easily adaptable output with high accuracy while reducing human bias, associated costs, and time involved to perform tasks [7]. However, there is still a necessity to improve the overall accuracy of the DFU detection and automatic classification, both external and internal ulcer boundaries, in a real world setting [20; 23]. Furthermore, as of 2020, no evidence was found for its implementation and use in a healthcare scenario [7]. An automated clinical workflow, based on temperature measurements is being established to detect and monitor diabetic foot disorders.