Rheumatoid Arthritis Progression Estimation using statistical shape model and support vector machine

Abstract

The number of rheumatoid arthritis (RA) patients is about 700,000 in Japan. The modified total sharp score (mTSS) calculated from hand X-ray image is a standard diagnosis method of RA progression but can be time-consuming for physicians. So, improving the diagnostic quality of RA patients requires a computer-aided diagnostic (CAD) system. We have previously proposed a CAD system, which detects finger joint positions using support vector machine, and estimates mTSS using support vector regression. This study improves the finger joint detection accuracy by introducing statistical shape model, which statistically models individual variety of spatial relationship among finger joints and fingertips. And, improving the mTSS estimation accuracy, we introduce a data cleansing process based on an image clustering method. Experimental results on radiographic images of the hands of 90 RA patients showed that the finger joints were detected with an accuracy of 94.5%.And the accuracy of the mTSS was also improved.