Convolutional Neural Networks for Differential Diagnosis of Raynaud’s Phenomenon Based on Hands Thermal Patterns

Abstract

Raynaud’s phenomenon (RP) is a microvessels’ disorder resulting in transient ischemia. It can be either primary or secondary to connective tissue diseases, such as systemic sclerosis. The differentiation between primary and secondary to systemic sclerosis is of paramount importance to set the proper therapeutic strategy. Thus far, thermal infrared imaging has been employed to accomplish this task by monitoring the finger temperature response to a controlled cold challenge. A completely automated methodology based on deep convolutional neural network is here introduced with the purpose of being able to differentiate systemic sclerosis from primary RP patients by relying uniquely on thermal images of the hands acquired at rest. The classification performance of such a method was compared to that of a three-dimensional convolutional neural network model implemented to classify thermal images of the hands recorded during rewarming from a cold challenge. No significant differences were found between the two procedures, thus ensuring the possibility to avoid the cold challenge. Moreover, the convolutional neural network models were compared with standard feature-based approaches and showed higher performances, thus overcoming the limitations related to the feature extraction (e.g., biases introduced by the operator). Such automated procedures can constitute promising tools for large scale screening of primary RP and secondary to systemic sclerosis in clinical practice.

Keywords

• deep learning
• neural network
• thermal imaging
• Raynaud phenomenon
• systemic sclerosis