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

医学 类风湿性关节炎 人工智能 疾病 深度学习 超声波 放射科 机器学习 医学物理学 内科学 计算机科学
作者
Xuelei He,Ming Wang,Chenyang Zhao,Qian Wang,Rui Zhang,Jian Liu,Yixiu Zhang,Zhenhong Qi,Na Su,Wei Yao,Yang Gui,George W. Kattawar,Xinping Tian,Xiaofeng Zeng,Yuxin Jiang,Kun Wang,Meng Yang
出处
期刊:Rheumatology [Oxford University Press]
卷期号:63 (3): 866-873 被引量:2
标识
DOI:10.1093/rheumatology/kead366
摘要

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.
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