A Novel Method of Synovitis Stratification in Ultrasound Using Machine Learning Algorithms: Results From Clinical Validation of the MEDUSA Project

摘要

Abstract Ultrasound is widely used in the diagnosis and follow-up of chronic arthritis. We present an evaluation of a novel automatic ultrasound diagnostic tool based on image recognition technology. Methods used in developing the algorithm are described elsewhere. For the purpose of evaluation, we collected 140 ultrasound images of metacarpophalangeal and proximal interphalangeal joints from patients with chronic arthritis. They were classified, according to hypertrophy size, into four stages (0–3) by three independent human observers and the algorithm. An agreement ratio was calculated between all observers and the standard derived from results of human staging using κ statistics. Results was significant in all pairs, with the highest p value of 3.9?×?10 –6 . κ coefficients were lower in algorithm/human pairs than between human assessors. The algorithm is effective in staging synovitis hypertrophy. It is, however, not mature enough to use in a daily practice because of limited accuracy and lack of color Doppler recognition. These limitations will be addressed in the future.