DIAGNOSIS OF METACARPOPHALANGEAL SYNOVITIS WITH MUSCULOSKELETAL ULTRASOUND IMAGES

Rheumatoid arthritis (RA) is a chronic autoimmune disease that can result in considerable disability and pain. The metacarpophalangeal (MCP) joint is the most common diseased joint in RA. In clinical practice, MCP synovitis is commonly diagnosed on the basis of musculoskeletal ultrasound (MSUS) images. However, because of the vague criteria, the consistency in grading MCP synovitis based on MSUS images fluctuates between ultrasound imaging practitioners. Therefore, a new method for diagnosis of MCP synovitis is needed. Deep learning has developed rapidly in the medical area, which often requires a large-scale data set. However, the total number of MCP-MSUS images fell far short of the demand, and the distribution of different medical grades of images was unbalanced. With use of the traditional image augmentation methods, the diversity of the data remains insufficient. In this study, a high-resolution generative adversarial network (HRGAN) method that generates enough images for network training and enriches the diversity of the training data set is described. In comparison experiments, our proposed diagnostic system based on MSUS images provided more consistent results than those provided by clinical physicians. As the proposed method is image relevant, this study might provide a reference for other medical image classification research with insufficient data sets. (E-mail: yuanjie@nju.edu.cn) (c) 2021 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

Automated summary

Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes significant disability and pain. Clinical diagnosis of MCP synovitis based on musculoskeletal ultrasound (MSUS) images lacks consistency among practitioners. This study proposes a high-resolution generative adversarial network (HRGAN) method to generate sufficient training images and improve data diversity, resulting in a more consistent diagnostic system compared to clinical physicians. The relevance of this method to medical image classification research with limited data sets is highlighted.