CAPI-detect: machine learning in capillaroscopy reveals new variables influencing diagnosis

Abstract

Objectives Nailfold videocapillaroscopy (NVC) is the gold standard for diagnosing systemic sclerosis (SSc) and differentiating primary from secondary Raynaud's phenomenon. The CAPI-Score algorithm, designed for simplicity, classifies capillaroscopy scleroderma patterns (CSPs) using a limited number of capillary variables. This study aims to develop a more advanced machine learning (ML) model to improve CSP identification by integrating a broader range of statistical variables while minimising examiner-related bias. Methods A total of 1,780 capillaroscopies were randomly and blindly analysed by 3–4 trained observers. Consensus was defined as agreement among all but one observer (partial consensus) or unanimous agreement (full consensus). Capillaroscopies with at least partial consensus were used to train ML-based classification models using CatBoost software, incorporating 24 capillary architecture-related variables extracted via automated NVC analysis. Validation sets were employed to assess model performance. Results Of the 1,490 capillaroscopies classified with consensus, 515 achieved full consensus. The model, evaluated on partial and full consensus datasets, achieved 0.912, 0.812, and 0.746 accuracy for distinguishing SSc from non-SSc, among SSc patterns, and between normal and non-specific patterns, respectively. When evaluated on full consensus only, accuracy improved to 0.910, 0.925, and 0.933. CAPI-Detect outperformed CAPI-Score, revealing novel capillary variables critical to ML-based classification. Conclusions CAPI-Detect, an ML-based model, provides an unbiased, quantitative analysis of capillary structure, shape, size, and density, significantly improving capillaroscopic pattern identification.