Finger Joint Detection Method for the Automatic Estimation of Rheumatoid Arthritis Progression Using Machine Learning

摘要

The number of Rheumatoid Arthritis (RA) patients increases recently in Japan. Early treatment improves patient's prognosis and Quality of Life. The appropriate treatment in accordance with RA progression is required for the better prognosis. The hand X-ray image based modified Total Sharp Score (mTSS) is widely used for the diagnosis of RA progression. The mTSS measurement is essential to achieve the appropriate treatment, but its assessment is time consumed. There are some finger joint detection and mTSS estimation methods for the fully automated mTSS measurement, which focus on the mild RA patients. This paper proposes the automatic joint detection method and discusses about the mTSS estimation for the mild-to-severe RA patients. Experimental results on 90 RA patients' hand X-ray images showed that the proposed method detected finger joints with accuracy of 91.8%, and estimated the erosion and JSN score with accuracy of 53.3% and 60.8%, respectively.