Title | RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning |
Author | |
Corresponding Author | Qiao, Hui; Wang, Qian; Xu, Feng; Dai, Qionghai; Yang, Meng |
Publication Years | 2022-09-09
|
DOI | |
Source Title | |
ISSN | 2666-3899
|
Volume | 3Issue:9 |
Abstract | 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. |
URL | [Source Record] |
Indexed By | |
Language | English
|
SUSTech Authorship | Others
|
Funding Project | International Science and Technology Cooperation Programme[2015DFA30440]
; National Natural Science Foundation of China["81421004","62071271","62021002","61727808","61971447","81301268"]
; National Key Technology R&D Program of China["2019YFC0840603","2017YFC0907601","2017YFC0907604","2017YFE0104200","2018YFA0704000"]
; Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences["2020-I2M-CT-B-035","2021-I2M-1-005"]
; Non-profit Central Research Institute Fund of the Chinese Academy of Medical Sciences[2021-PT320-002]
; Beijing Municipal Natural Science Foundation["JQ18023","JQ19015","JQ21012"]
|
WOS Research Area | Computer Science
|
WOS Subject | Computer Science, Artificial Intelligence
; Computer Science, Information Systems
; Computer Science, Interdisciplinary Applications
|
WOS Accession No | WOS:000898561500003
|
Publisher | |
Data Source | Web of Science
|
Citation statistics |
Cited Times [WOS]:0
|
Document Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/424064 |
Department | Shenzhen People's Hospital |
Affiliation | 1.Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China 2.Tsinghua Univ, Inst Brain & Cognit Sci, Beijing 100084, Peoples R China 3.Chinese Acad Med Sci & Peking Union Med Coll, Dept Ultrasound, State Key Lab Complex Severe & Rare Dis, Peking Union Med Coll Hosp, Beijing 100730, Peoples R China 4.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 5.Jinan Univ, Affiliated Hosp 1, Southern Univ Sci & Technol, Shenzhen Peoples Hosp,Dept Ultrasound,Clin Coll 2, Shenzhen 518020, Peoples R China |
Recommended Citation GB/T 7714 |
Zhou, Zhanping,Zhao, Chenyang,Qiao, Hui,et al. RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning[J]. Patterns,2022,3(9).
|
APA |
Zhou, Zhanping.,Zhao, Chenyang.,Qiao, Hui.,Wang, Ming.,Guo, Yuchen.,...&Yang, Meng.(2022).RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning.Patterns,3(9).
|
MLA |
Zhou, Zhanping,et al."RATING: Medical knowledge-guided rheumatoid arthritis assessment from multimodal ultrasound images via deep learning".Patterns 3.9(2022).
|
Files in This Item: | There are no files associated with this item. |
|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment