## 4 Discussion  
Diabetic foot ulcers are costly and debilitating and have serious consequences for people with diabetes [37]. All diabetic patients should be carefully and thoroughly educated about preventive measures and foot care [5]. However, the traditional process of diagnosis of DFU by clinicians and DFU specialists is very expensive and time-consuming. Therefore, deep learning in medical imaging opens corridors for the automatic diagnosis of DFU [38]. Given the complex nature of DFUs, AI methodologies seem well suited to address aspects such as timely screening to identify the risk of foot ulcers (or worse, amputation) based on appropriate sensor technologies [39]. Our review of the papers found that DFUs can be identified in different ways. The most used classifiers in the reviewed papers are SVM and KNN. [PERSON] et al. [24] found that SVM is better than the other algorithms in point of MCC, which is 0.875, and F-measure, which is 0.938. The performance of KNN and Naive Bayes were comparable to each other. In the study, RF was the most efficient in terms of sensitivity and SVM was the most efficient in terms of highest specificity, which was 93.8%. [PERSON] et al. [26] included ELM and ANN in the research, in parallel to SVM and KNN. The ELM achieved the highest accuracy, followed by the SVM with 92.31%. The ANN and KNN were comparable, both achieving an accuracy of 84.62%. In a literature review by [PERSON] et al. [28], all included models achieved accuracies above 90%. SVM and KNN have also been used for risk prediction based on thermography. In a study by [PERSON] [30], although the SVM model obtained satisfactory results, it did so after a feature extraction procedure, which we would like  
Figure 2: Artificial intelligence prediction techniques for risk of developing diabetic foot. to avoid. [PERSON] et al. [31] found that KNN achieved the highest performance, improving classification accuracy by 4.3% compared to using SVM. Another effective technique for interpreting the learned features of CNN layers is t-distributed stochastic neighborhood embedding (t-SNE) [40], which has been used to visualize clusters of heart rate data with respect to glucose levels. The use of t-SNE can also be generalized to other CNN applications, such as DFU detection, in order to qualitatively analyze the extracted feature maps. In addition, a recent study has also verified the consistency of neural network models with respect to glucose and insulin dynamics [41]. Similar approaches can be used to analyze the performance of DNNs and further improve interpretability [42]. The most common variables included in the prognostic model are age, type of diabetes, body mass index (BMI), and type of diabetes. In the studies by [PERSON] et al. [23] and [PERSON] et al. [29], only ANN models were used. In the first study, they reported a sensitivity of 71%, a specificity of 100%, and an accuracy of 90%. In the second study, they report that the model had a sensitivity of 92.3% and a specificity of 93.5%. Many authors attempt to classify foot thermograms using asymmetric analysis, which consists of comparing the temperature of the foot with that of the contralateral foot [31]. Several papers report that thermography is useful for detecting changes in sole temperature that could increase the risk of pressure ulcers. [PERSON] et al. (2020) discovered that the CNN classifiers require an additional data augmentation step for three structures (GoogLeNet, AlexNet, and DFTNet) [30]. The highest values of sensitivity, specificity, AUC (area under the curve), and accuracy were obtained with classifiers such as SVM and for the CNN structures in almost all pairs of classes compared, especially in the well-separated classes. The best results of DFTNet can be attributed to the specific network design for this type of images. Although the GoogLeNet and AlexNet network structures are more complex and supposed to be better classifiers, they were trained with a different type of images.