Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images

Author:

He Xuelei123,Wang Ming1,Zhao Chenyang1,Wang Qian4ORCID,Zhang Rui1,Liu Jian5,Zhang Yixiu1,Qi Zhenhong1,Su Na1,Wei Yao1,Gui Yang1,Li Jianchu1,Tian Xinping4ORCID,Zeng Xiaofeng4ORCID,Jiang Yuxin1,Wang Kun3,Yang Meng1

Affiliation:

1. Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing, People's Republic of China

2. School of Information Sciences and Technology, Northwest University , Xi’an, Shaanxi Province, People’s Republic of China

3. CAS Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences , Beijing, People's Republic of China

4. Department of Rheumatology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing, People's Republic of China

5. Department of Rheumatology and Immunology, Aerospace Center Hospital, Peking University Aerospace School of Clinical Medicine , Beijing, People's Republic of China

Abstract

Abstract Objectives We aimed to investigate the value of deep learning (DL) models based on multimodal ultrasonographic (US) images to quantify RA activity. Methods Static greyscale (SGS), dynamic greyscale (DGS), static power Doppler (SPD) and dynamic power Doppler (DPD) US images were collected and evaluated by two expert radiologists according to the EULAR–OMERACT Synovitis Scoring system. Four DL models were developed based on the ResNet-type structure, evaluated on two separate test cohorts, and finally compared with the performance of 12 radiologists with different levels of experience. Results In total, 1244 images were used for the model training, and 152 and 354 for testing (cohort 1 and 2, respectively). The best-performing models for the scores of 0/1/2/3 were the DPD, SGS, DGS and SPD models, respectively (Area Under the receiver operating characteristic Curve [AUC] = 0.87/0.95/0.74/0.95; no significant differences). All the DL models provided results comparable to the experienced radiologists on a per-image basis (intraclass correlation coefficient: 0.239–0.756, P < 0.05). The SPD model performed better than the SGS one on test cohort 1 (score of 0/2/3: AUC = 0.82/0.67/0.95 vs 0.66/0.66/0.75, respectively) and test cohort 2 (score of 0: AUC = 0.89 vs 0.81). The dynamic DL models performed better than the static ones in most of the scoring processes and were more accurate than the most of senior radiologists, especially the DPD model. Conclusion DL models based on multimodal US images allow a quantitative and objective assessment of RA activity. Dynamic DL models in particular have potential value in assisting radiologists to improve the accuracy of RA US-based grading.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Pharmacology (medical),Rheumatology

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