Development and validation of a combined ultrasound−radiomics model for assessing rheumatoid arthritis disease activity: a prospective, two−center diagnostic study

ObjectiveTo develop and evaluate a combined model integrating musculoskeletal ultrasound (MSK US) with a machine learning (ML) algorithm for assessing disease activity in rheumatoid arthritis (RA).MethodsA total of 203 patients with clinically confirmed RA were prospectively enrolled from December 2023 to September 2025. A cohort of 142 patients from the First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology served as the training cohort, while 61 patients from Affiliated Hospital of Traditional Chinese Medicine, Xinjiang Medical University (Fourth Clinical Medical College, Xinjiang Medical University) constituted the independent external test cohort.

Three predictive models were developed: (1) an MSK US model incorporating two-dimensional grayscale ultrasound, power Doppler ultrasound (PDUS), and superb micro-vascular imaging (SMI); (2) a radiomics model based on two-dimensional grayscale images using the extremely randomized trees (ExtraTrees) algorithm; and (3) a combined model integrating the first two. Model performance in assessing RA disease activity was evaluated and compared using receiver operating characteristic (ROC) curve analysis.