Leveraging Transfer Learning and Monte Carlo Dropout for Uncertainty Informed NIRS-based Detection of Systemic Sclerosis Hand Perfusion Patterns

Early diagnosis of systemic sclerosis (SSc) is critical for early intervention and improved patient outcomes. This study explores the integration of near-infrared spectroscopy (NIRS) with deep learning for classification of SSc patients based on hand perfusion patterns. A probabilistic convolutional neural network (CNN) using MobileNetV2 with transfer learning was employed to analyze NIRS-derived oxygen saturation maps. The model achieved a test accuracy of 87.5%, demonstrating strong classification performance despite the limited data set. The Monte Carlo Dropout method was incorporated to evaluate the predictive uncertainty, providing valuable insight into the confidence of the model and its ability to detect potential out-of-distribution (OOD) inputs. The different confidence levels observed in the training, validation, and test datasets highlight the importance of uncertainty estimation in assessing model reliability and robustness. These results underscore the feasibility of deep learning-based NIRS analysis as a noninvasive and automated tool to detect microvascular dysfunction in SSc patients. Future work should focus on expanding the dataset, integrating multimodal imaging, and exploring advanced architectures to improve generalizability and clinical applicability.Clinical Relevance- Early detection of systemic sclerosis is essential for better outcomes. NIRS, combined with deep learning, offers a non-invasive, objective, and efficient tool for improving diagnosis and monitoring of microvascular dysfunction in systemic sclerosis patients.