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

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.

Methods

The algorithm, originally developed during the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM) challenge, was trained to predict expert-curated Sharp-van der Heijde total scores in hand and foot radiographs from two previous clinical studies (n = 367). We externally validated AuRA against data (n = 205) from Turku University Hospital and comp

SUBMITTER: Venalainen MS 

PROVIDER: S-EPMC11879318 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature