Deep learning enables automatic detection of joint damage progression in rheumatoid arthritis-model development and external validation.

Journal: Rheumatology (Oxford, England)
PMID: 38597875

Abstract

OBJECTIVES: Although deep learning has demonstrated substantial potential in automatic quantification of joint damage in RA, evidence for detecting longitudinal changes at an individual patient level is lacking. Here, we introduce and externally validate our automated RA scoring algorithm (AuRA), and demonstrate its utility for monitoring radiographic progression in a real-world setting.

Authors

  • Mikko S Venäläinen
    Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
  • Alexander Biehl
    Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
  • Milja Holstila
    Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland.
  • Laura Kuusalo
    Centre for Rheumatology and Clinical Immunology, Division of Medicine, University of Turku and Turku University Hospital, Turku, Finland.
  • Laura L Elo
    Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland.

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