Automatic Carpal Site Detection Method for Evaluation of Rheumatoid Arthritis Using Deep Learning

Approximately 600,000 to 1,000,000 patients are diagnosed with rheumatoid arthritis (RA) in Japan. To provide appropriate treatment, it is necessary to accurately measure the progression of RA by diagnosing the disease several times a year. The modified total sharp score (mTSS) calculated from hand X-ray images is a standard diagnostic method for RA progression. However, this diagnostic method is timeconsuming as the scores are rated at as many as 16 points per hand. Accordingly, in order to shorten the diagnosis time of RA patients and improve the quality of diagnosis, the development of computeraided diagnosis (CAD) systems is expected. We have previously proposed a CAD system that can detect finger joint positions using a support vector machine and can estimate the mTSS using ridge regression. In this study, we propose a fully automatic detection method of RA score evaluation points in the carpal site from simple hand X-ray images using deep learning. The deep learning. Next, the RA evaluation points are automatically determined from each segment based on prior knowledge. Experimental results on X-ray imdetected with an average error of 25 pixels. This study physician.

Automated summary

This study proposes a fully automatic detection method of rheumatoid arthritis score evaluation points using deep learning, which can significantly shorten the diagnosis time and improve the diagnosis quality.