Deep Learning Performance in Analyzing Nailfold Videocapillaroscopy Images in Systemic Sclerosis

Background/Objectives: The aim of this study was to classify nailfold videocapillaroscopy (NVC) images obtained from patients with systemic sclerosis (SSc) and healthy individuals using deep learning methods. Methods: Between January and June 2025, 1280 NVC images were recorded from 50 SSc and 30 healthy individuals. The images were classified by two rheumatologists as normal, early, active, and late-stage SSc patterns. After removing 191 unclassifiable and 112 low-quality images, 977 usable images remained. To ensure balanced classes, 245 normal images were excluded. The final dataset was split into training (70%), validation (20%), and test (10%) sets. Six different deep learning models (MobileNetV3Large, ResNet152V2, Xception, VGG-19, InceptionV3, and NASNetLarge) with varying levels of complexity and computational efficiency were selected to compare their performance. Accuracy, precision, recall, F1 Score, and cross-entropy loss were employed as performance metrics. These metrics are commonly used in the literature to evaluate the effectiveness of classification models. Results: Deep learning models achieved an accuracy ranging from 90.6% to 98.9%, a precision of 93.4% to 98.9%, a recall of 90.6% to 98.8%, an F1 score of 92% to 98.9%, and an ROC AUC performance between 99% and 100%. InceptionV3 demonstrated the best performance with an accuracy of 98.95%, a precision of 98.94%, a recall of 98.80%, and an F1 score of 98.88%. In terms of test loss, the lowest values were observed in the InceptionV3 and NasNetLarge models, both with a loss of 0.03. Overall, the ROC AUC values for all models ranged between 98.99% and 100%, indicating competitive performance. Conclusions: The current findings suggest that deep learning methods may be capable of classifying NVC images as accurately as experienced rheumatologists.