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In Patterns (New York, N.Y.)

Multimodal ultrasound has demonstrated its power in the clinical assessment of rheumatoid arthritis (RA). However, for radiologists, it requires strong experience. In this paper, we propose a rheumatoid arthritis knowledge guided (RATING) system that automatically scores the RA activity and generates interpretable features to assist radiologists' decision-making based on deep learning. RATING leverages the complementary advantages of multimodal ultrasound images and solves the limited training data problem with self-supervised pretraining. RATING outperforms all of the existing methods, achieving an accuracy of 86.1% on a prospective test dataset and 85.0% on an external test dataset. A reader study demonstrates that the RATING system improves the average accuracy of 10 radiologists from 41.4% to 64.0%. As an assistive tool, not only can RATING indicate the possible lesions and enhance the diagnostic performance with multimodal ultrasound but it can also enlighten the road to human-machine collaboration in healthcare.

Zhou Zhanping, Zhao Chenyang, Qiao Hui, Wang Ming, Guo Yuchen, Wang Qian, Zhang Rui, Wu Huaiyu, Dong Fajin, Qi Zhenhong, Li Jianchu, Tian Xinping, Zeng Xiaofeng, Jiang Yuxin, Xu Feng, Dai Qionghai, Yang Meng


deep learning, human-machine collaboration, multimodal ultrasound, rheumatoid arthritis