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Objectives The first objective of this study was to implement and assess the performance and reliability of a vision transformer (ViT)-based deep-learning model, an 'off-the-shelf' artificial intelligence solution, for identifying distinct signs of microangiopathy in nailfold capilloroscopy (NFC) images of patients with SSc. The second objective was to compare the ViT's analysis performance with that of practising rheumatologists. Methods NFC images of patients prospectively enrolled in our European Scleroderma Trials and Research group (EUSTAR) and Very Early Diagnosis of Systemic Sclerosis (VEDOSS) local registries were used. The primary outcome investigated was the ViT's classification performance for identifying disease-associated changes (enlarged capillaries, giant capillaries, capillary loss, microhaemorrhages) and the presence of the scleroderma pattern in these images using a cross-fold validation setting. The secondary outcome involved a comparison of the ViT's performance vs that of rheumatologists on a reliability set, consisting of a subset of 464 NFC images with majority vote–derived ground-truth labels. Results We analysed 17 126 NFC images derived from 234 EUSTAR and 55 VEDOSS patients. The ViT had good performance in identifying the various microangiopathic changes in capillaries by NFC [area under the curve (AUC) from 81.8% to 84.5%]. In the reliability set, the rheumatologists reached a higher average accuracy, as well as a better trade-off between sensitivity and specificity compared with the ViT. However, the annotators' performance was variable, and one out of four rheumatologists showed equal or lower classification measures compared with the ViT. Conclusions The ViT is a modern, well-performing and readily available tool for assessing patterns of microangiopathy on NFC images, and it may assist rheumatologists in generating consistent and high-quality NFC reports; however, the final diagnosis of a scleroderma pattern in any individual case needs the judgement of an experienced observer. [ABSTRACT FROM AUTHOR]

Objective: To address the hypothesis that very early patients with systemic sclerosis (SSc) are a heterogeneous group with mild or early disease, we analyzed the extent of heterogeneity in clinical, epidemiological, and immunological characteristics of these patients.Methods: We performed an analysis of very early SSc patients from the Zurich cohort, who fulfilled neither the 2013 American College of Rheumatology (ACR)/European League Against Rheumatism nor the 1980 ACR classification criteria, but had a clinical expert diagnosis of SSc with Raynaud phenomenon (RP) and additional features of SSc (puffy fingers, SSc-specific antibodies, SSc pattern on nailfold capillaroscopy, or any organ involvement characteristic for SSc). Disease duration was defined from first RP symptom.Results: One hundred and two patients fulfilled the inclusion criteria and were analyzed. Their clinical presentation was heterogeneous with the large majority presenting with RP, antinuclear antibodies, and nailfold capillaroscopy changes, but with varying presentations of other features such as SSc-specific antibodies and early signs of organ involvement. While 54.1% (52/96) of patients had a disease duration of < 5 years, as many as 29.1% (28/96) of patients had a disease duration of > 10 years, indicating long-standing mild disease. Patients with very early, potentially progressive disease did not differ from patients with long-standing mild disease in terms of their clinical features at first presentation.Conclusion: This study showed that patients with very early SSc are a mixture with mild or early disease. This needs to be considered in clinical practice for risk stratification and for the study design of patients considered as early SSc. [ABSTRACT FROM AUTHOR]