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

Journal: Rheumatology (Oxford, England)
Published Date: Jun 1, 2025

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.

Authors

  • Gema M Lledó-Ibáñez
    Department of Autoimmune Diseases, Institut Clinic de Medicina i Dermatologia, Hospital Clínic de Barcelona, Barcelona, Spain.
  • Luis Sáez Comet
    Department of Internal Medicine, Hospital Universitario Miguel Servet, Zaragoza, Spain.
  • Mayka Freire Dapena
    Department of Internal Medicine, Hospital Clínico Universitario de SAntiago de Compostela, La Coruña, Spain.
  • Miguel Mesa Navas
    Rheumatology Department, Clínica Universitaria Bolivariana, Universidad Pontificia Bolivariana, Medellín, Colombia.
  • Miguel Martín Cascón
    Department of Internal Medicine, Hospital General Universitario Morales Meseguer, Murcia, Spain.
  • Alfredo Guillén Del Castillo
    Department of Internal Medicine, Hospital Universitario Vall d'Hebron, Barcelona, Spain.
  • Carmen Pilar Simeon
    Department of Internal Medicine, Hospital Universitario Vall d'Hebron, Barcelona, Spain.
  • Elena Martinez Robles
    Department of Internal Medicine, Hospital Universitario La Paz, Madrid, Spain.
  • José Todolí Parra
    Department of Internal Medicine, Hospital Universitario La Fé, Valencia, Spain.
  • Diana Cristina Varela
    Rheumatology Department, Hospital General de Medellín, Medellín, Colombia.
  • Génesis Maldonado
    Vanderbilt University, Nashville, Tennessee, USA.
  • Adela Marín
    Department of Internal Medicine, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain.
  • Laura Pérez Abad
    Department of Internal Medicine, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain.
  • Jimena Aramburu
    Department of Internal Medicine, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain.
  • Laura Vela
    Department of Internal Medicine, Hospital Universitario Miguel Servet, Zaragoza, Spain.
  • Eduardo Ramos Ibáñez
    Ingeniero Informático, Universidad de Zaragoza, Zaragoza, Spain.
  • Borja Del Carmelo Gracia Tello
    Department of Internal Medicine, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain.

Keywords

External Resources

Popular Topics
Recent Journals