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Self-supervised learning assisted diagnosis for mitral regurgitation severity classification based on color Doppler echocardiography

文献类型：期刊论文

作者Yang, Feifei8,9,10; Zhu, Jiuwen7; Wang, Junfeng6; Zhang, Liwei5; Wang, Wenjun10; Chen, Xu4,10; Lin, Xixiang4,10; Wang, Qiushuang5; Burkhoff, Daniel3; Zhou, S. Kevin1,2,7
刊名ANNALS OF TRANSLATIONAL MEDICINE
出版日期2021-10-26
页码14
关键词Mitral regurgitation (MR) self-supervised learning (SSL) color Doppler echocardiography mitral regurgitation grading (MR grading)
ISSN号2305-5839
DOI10.21037/atm-21-3449
英文摘要Background: Mitral regurgitation (MR) is the most common valve lesion worldwide. However, the quantitative assessment of MR severity based on current guidelines is challenging and time-consuming; strict adherence to applying these guidelines is therefore relatively infrequent. We aimed to develop an automatic, reliable and reproducible artificial intelligence (AI) diagnostic system to assist physicians in grading MR severity based on color video Doppler echocardiography via a self-supervised learning (SSL) algorithm. Methods: We constructed a retrospective cohort of 2,766 consecutive echocardiographic studies of patients with MR diagnosed based on clinical criteria from two hospitals in China. One hundred and forty-eight studies with reference standards were selected in the main analysis and also served as the test set for the AI segmentation model. Five hundred and ninety-two and 148 studies were selected with stratified random sampling as the training and validation datasets, respectively. The self-supervised algorithm captures features and segments the MR jet and left atrium (LA) area, and the output is used to assist physicians in MR severity grading. The diagnostic performance of physicians without and with the support from AI was estimated and compared. Results: The performance of SSL algorithm yielded 89.2% and 85.3% average segmentation dice similarity coefficient (DICE) on the validation and test datasets, which achieved 6.2% and 8.1% improvement compared to Residual U-shape Network (ResNet-UNet), respectively. When physicians were provided the output of algorithm for grading MR severity, the sensitivity increased from 77.0% (95% CI: 70.9-82.1%) to 86.7% (95% CI: 80.3-91.2%) and the specificity was largely unchanged: 91.5% (95% CI: 87.8-94.1%) vs. Conclusions: This study provides a new, practical, accurate, plug-and-play AI-assisted approach for assisting physicians in MR severity grading that can be easily implemented in clinical practice.
资助项目Beijing Natural Science and Technology Foundation[7202198] ; Ministry of Industry and Information Technology of China[2020-0103-3-1]
WOS研究方向Oncology ; Research & Experimental Medicine
语种英语
WOS记录号WOS:000730237900001
出版者AME PUBL CO
源URL[http://119.78.100.204/handle/2XEOYT63/18067]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者He, Kunlun
作者单位1.Univ Sci & Technol China, Suzhou Inst Adv Res, Suzhou, Peoples R China
2.Univ Sci & Technol China, MTRACLE Ctr, Sch Biomed Engn, Suzhou, Peoples R China
3.Cardiovasc Res Fdn, New York, NY USA
4.China Cardiovasc Res Fdn, Med Sch Chinese PLA, New York, NY USA
5.Chinese PLA Gen & Lospital, Dept Cardiol, Med Ctr 4, Beijing, Peoples R China
6.Univ Utrecht, Utrecht Inst Pharmaceut Sci, Div Pharmacoepidemiol & Clin Pharmacol, Utrecht, Netherlands
7.Univ Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, MIRACLE Grp, Beijing, Peoples R China
8.Chinese Peoples Liberat Army Gen Hosp, Minist Ind & Informat Technol Biomed Engn & Trans, Key Lab, Beijing, Peoples R China
9.Chinese Peoples Liberat Army Gen Hosp, Beijing Key Lab Precis Med Chron Heart Failure, Beijing, Peoples R China
10.Chinese Peoples Liberat Army Gen Hosp, Med Big Data Res Ctr, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Feifei,Zhu, Jiuwen,Wang, Junfeng,et al. Self-supervised learning assisted diagnosis for mitral regurgitation severity classification based on color Doppler echocardiography[J]. ANNALS OF TRANSLATIONAL MEDICINE,2021:14.
APA Yang, Feifei.,Zhu, Jiuwen.,Wang, Junfeng.,Zhang, Liwei.,Wang, Wenjun.,...&He, Kunlun.(2021).Self-supervised learning assisted diagnosis for mitral regurgitation severity classification based on color Doppler echocardiography.ANNALS OF TRANSLATIONAL MEDICINE,14.
MLA Yang, Feifei,et al."Self-supervised learning assisted diagnosis for mitral regurgitation severity classification based on color Doppler echocardiography".ANNALS OF TRANSLATIONAL MEDICINE (2021):14.

入库方式： OAI收割

来源：计算技术研究所

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