Portal de investigación
CAPI-Detect: machine learning in capillaroscopy reveals new variables influencing diagnosis.
Fecha de publicación:
Fecha Ahead of Print:
Autores de IIS La Fe
Participantes ajenos a IIS La Fe
- Lledó-Ibáñez GM
- Sáez Comet L
- Freire Dapena M
- Mesa Navas M
- Martín Cascón M
- Guillén Del Castillo A
- Simeon CP
- Martinez Robles E
- Varela DC
- Maldonado G
- Marín A
- Pérez Abad L
- Aramburu J
- Vela L
- Ramos Ibáñez E
- Del Carmelo Gracia Tello B
Grupos
Abstract
OBJECTIVES: Nailfold videocapillaroscopy (NVC) is the gold standard for diagnosing SSc and differentiating primary from secondary RP. 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 minimizing examiner-related bias. METHODS: A total of 1780 capillaroscopies were randomly and blindly analysed by three to four 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 1490 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.
© The Author(s) 2025. Published by Oxford University Press on behalf of the British Society for Rheumatology.
Datos de la publicación
- ISSN/ISSNe:
- 1462-0324, 1462-0332
- Tipo:
- Article
- Páginas:
- 3667-3675
- PubMed:
- 39918978
- Enlace a otro recurso:
- www.scopus.com
- Factor de Impacto:
- 1,563 SCImago ℠
- Cuartil:
- Q1 SCImago ℠
RHEUMATOLOGY OXFORD UNIV PRESS
Citas Recibidas en Web of Science: 19
Documentos
- No hay documentos
Filiaciones
Keywords
- CatBoost algorithm; disease pattern; examiner consensus; machine learning-based model; nailfold capillaroscopy; quantitative analysis; software-based analysis; systemic sclerosis
Cita
Lledó GM,Sáez L,Freire M,Mesa M,Martín M,Guillén Del Castillo A,Simeon CP,Martinez E,TODOLÍ J,Varela DC,Maldonado G,Marín A,Pérez L,Aramburu J,Vela L,Ramos E,Del Carmelo B. CAPI-Detect: machine learning in capillaroscopy reveals new variables influencing diagnosis. Rheumatology. 2025. 64. (6):p. 3667-3675. IF:4,700. (1).