Recognition System of Positions of Joints of Hands in an X-ray photograph to Develop an Automatic Evaluation System for Rheumatoid Arthritis Using Machine Learning

Abstract

Rheumatoid arthritis is a disease of the joint that are destroyed, it is difficult for a patient with serious condition to live his or her everyday life. It is important to evaluate the condition of rheumatoid arthritis in order to give the suitable treatment. However, the evaluation task takes time and is necessary to experience of the doctor. Therefore, it is desirable to develop the automatic evaluation system. Our objective goal is to develop the automatic evaluation system that can be updated using the revised data obtained by the doctor. It is clear that the evaluation system of the doctor consists of the recognition system and the classification system. This paper proposes the recognition of the joint in the X-ray photograph using the machine learning. To realize the system, we separate the recognition system into four procedure; convert procedure, training procedure, validation procedure, and feedback procedure. And the effectiveness of the proposed system is investigated using the real X-ray photographs of the patients with rheumatoid arthritis. As a result, it is clear that a lot of correct data are necessary to improve the accuracy. Therefore, it is clear that the it is more effective to improve the accuracy, if the revised data obtained by the doctor are feedbacked to the training system.