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

doi:10.1093/rheumatology/kead366

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images
	He, Xuelei 2,3,7; Wang, Ming 3; Zhao, Chenyang 3; Wang, Qian 6; Zhang, Rui 3; Liu, Jian 5; Zhang, Yixiu 3; Qi, Zhenhong 3; Su, Na 3; Wei, Yao 3
刊名	RHEUMATOLOGY
	2023-07-20
页码	8
关键词	deep learning RA activity scoring multimodal ultrasonography
ISSN号	1462-0324
DOI	10.1093/rheumatology/kead366
通讯作者	Wang, Kun(kun.wang@ia.ac.cn) ; Yang, Meng(yangmeng_pumch@126.com)
英文摘要	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.
资助项目	National Natural Science Foundation of China[U22A2023] ; National Natural Science Foundation of China[61971447] ; National Natural Science Foundation of China[81421004] ; National Natural Science Foundation of China[81301268] ; Beijing Natural Science Foundation[JQ18023] ; Fundamental Research Funds for the Central Public-interest Scientific Institution of Chinese Academy of Medical Sciences[2021-PT320-002] ; CAMS Innovation Fund for Medical Sciences (CIFMS)[2021-I2M-1005] ; National High Level Hospital Clinical Research Funding[2022-PUMCH-D-002] ; National High Level Hospital Clinical Research Funding[2022-PUMCH-C-009] ; National High Level Hospital Clinical Research Funding[2022-PUMCH-A-225] ; National High Level Hospital Clinical Research Funding[2022-PUMCH-B-064]
WOS关键词	EULAR RECOMMENDATIONS ; CLINICAL MANAGEMENT ; RELIABILITY ; INTRA ; ULTRASONOGRAPHY ; SYNOVITIS
WOS研究方向	Rheumatology
语种	英语
出版者	OXFORD UNIV PRESS
WOS记录号	WOS:001051223500001
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Fundamental Research Funds for the Central Public-interest Scientific Institution of Chinese Academy of Medical Sciences ; CAMS Innovation Fund for Medical Sciences (CIFMS) ; National High Level Hospital Clinical Research Funding
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54056]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Wang, Kun; Yang, Meng
作者单位	1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Zhongguancun East Rd 95, Beijing 100190, Peoples R China 2.Northwest Univ, Sch Informat Sci & Technol, Xian, Shaanxi, Peoples R China 3.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Ultrasound, State Key Lab Complex Severe & Rare Dis, Beijing, Peoples R China 4.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Ultrasound, State Key Lab Complex Severe & Rare Dis, Shuaifuyuan 1, Beijing 100730, Peoples R China 5.Peking Univ Aerosp, Aerosp Ctr Hosp, Sch Clin Med, Dept Rheumatol & Immunol, Beijing, Peoples R China 6.Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Rheumatol, State Key Lab Complex Severe & Rare Dis, Beijing, Peoples R China 7.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	He, Xuelei,Wang, Ming,Zhao, Chenyang,et al. Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images[J]. RHEUMATOLOGY,2023:8.
APA	He, Xuelei.,Wang, Ming.,Zhao, Chenyang.,Wang, Qian.,Zhang, Rui.,...&Yang, Meng.(2023).Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images.RHEUMATOLOGY,8.
MLA	He, Xuelei,et al."Deep learning-based automatic scoring models for the disease activity of rheumatoid arthritis based on multimodal ultrasound images".RHEUMATOLOGY (2023):8.