## 4 Discussion
### Comparisons with Previous Studies and Discussion on Machine Learning Results  
Several research studies focused on the development of reliable methods able to differentiate between PRP patients and RP secondary to SSc patients with high specificity. Since changes in temperature pattern of the finger and toes are a clinical manifestation of RP, the evaluation of the finger thermoregulatory impairment is essential to detect the presence of the disease and to differentiate the two forms of this disorder. To this end, thermal IR imaging has been widely used as a support to clinical diagnosis. However, the thermographic protocols often incorporate some form of temperature challenge, usually cold, and need to be performed by an experienced operator. For instance, [PERSON] et al. employed thermal IR imaging together with cold stress procedure to classify RP patients [58]. The authors used a linear discriminant analysis as a classification method. The best result obtained on the classification of RP, in primary and secondary reached an accuracy of 0.80. [PERSON] et al., performed RP classification through the use of thermal IR imaging and cold challenge procedure [19]. The overall classification outcome of 0.87 of correctly classified participants was achieved by employing a multiple logistic regression algorithm followed by a receiver operating characteristic curve analysis. Moreover, studies such as [PERSON] et al., demonstrated a statistically significant difference between the thermal IR time course of RP patients versus healthy ones during the recovery from a cold challenge [59]. Similar results were obtained in [2; 60]. Recent studies explored the possibility to classify RP patients based on their hands' temperature in baseline condition [61; 62]. [PERSON] et al. demonstrated that the baseline nail fold temperature was significantly lower in RP patients than in controls [48]. [PERSON] et al., proved that at baseline, higher temperatures at the distal interphalangeal joint and lower temperatures at metacarpophalangeal joints were observed in PRP compared to the secondary RP [61]. A considerable step forward would be the implementation of a completely automated procedure to avoid the use of a cold challenge and limit human intervention. To these aims, an automated data-driven DCNNs classifier that allows differentiation among PRP, SSc, and HC based on their hands' baseline IR image is presented in this study. The classifier's performance was compared to those of a feature-based approach. In addition, the DCNN model for IR images at rest was compared to a 3D-DCNN model fed with the hands' IR images acquired during the whole CCP. Each implemented model was cross-checked for performances evaluation. Regarding the basal condition, the DCNN implemented was able to classify PRP, SSc, and HC with a high degree of accuracy, i.e., an overall accuracy of 0.84, an overall sensitivity of 0.83, and specificity of 0.91. On the other hand, the feature-based approaches achieved an overall accuracy of 0.77, sensitivity of 0.77, and specificity of 0.88. With respect to the CCP, the 3D-DCNN model was able to classify PRP, SSc, and HC with an overall accuracy of 0.88, sensitivity of 0.89, and specificity of 0.94, whereas the SVC delivered a classification with an overall accuracy of 0.83, a sensitivity of 0.80, and specificity of 0.90. An investigatory analysis of the feature maps resulting from the convolutional layers (Figures 3 and 5) permits an insight on the feature extraction process performed by the DCNN and 3D-DCNN models. Both models seem to focus on the difference among fingers and dorsum temperature highlighting those fingers where the difference is smaller to differentiate between HC, SSc, and PRP participants. Although the accuracy of the classification performed on the IR imaging recorded during CCP was better than the accuracy achieved by classifying baseline IR images only, the differences were not statistically significant.