Advances in the Application of Artificial Intelligence in Fetal Echocardiography

被引：16
作者：
Zhang, Junmin [1 ,2 ,3 ]
Xiao, Sushan [1 ,2 ,3 ]
Zhu, Ye [1 ,2 ,3 ]
Zhang, Zisang [1 ,2 ,3 ]
Cao, Haiyan [1 ,2 ,3 ]
Xie, Mingxing [1 ,2 ,3 ]
Zhang, Li [1 ,2 ,3 ,4 ]
机构：
[1] Huazhong Univ Sci & Technol, Union Hosp, Tongji Med Coll, Dept Ultrasound Med, Wuhan, Peoples R China
[2] Clin Res Ctr Med Imaging Hubei Prov, Wuhan, Peoples R China
[3] Hubei Prov Key Lab Mol Imaging, Wuhan, Peoples R China
[4] Wuhan Union Hosp, 1277 Jiefang Ave, Wuhan 430022, Peoples R China
来源：
JOURNAL OF THE AMERICAN SOCIETY OF ECHOCARDIOGRAPHY | 2024年 / 37卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Ultrasonography; Fetal screening; Congenital heart disease; Deep learning; ACQUIRE CARDIAC VOLUMES; 4-DIMENSIONAL ULTRASOUND; SONOGRAPHIC EXAMINATION; LEARNING-MODEL; CLASSIFICATION; LOCALIZATION; DISPLACEMENT; DISEASE; QUALITY; FINE;
D O I：
10.1016/j.echo.2023.12.013
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning can significantly improve fetal survival rates. Echocardiography is one of the most accessible and widely used diagnostic tools in the diagnosis of fetal congenital heart disease. However, traditional fetal echocardiography has limitations due to fetal, maternal, and ultrasound equipment factors and is highly dependent on the skill level of the operator. Artificial intelligence (AI) technology, with its rapid development utilizing advanced computer algorithms, has great potential to empower sonographers in time-saving and accurate diagnosis and to bridge the skill gap in different regions. In recent years, AI-assisted fetal echocardiography has been successfully applied to a wide range of ultrasound diagnoses. This review systematically reviews the applications of AI in the field of fetal echocardiography over the years in terms of image processing, biometrics, and disease diagnosis and provides an outlook for future research. (J Am Soc Echocardiogr 2024;37:550-61.)
引用
收藏
页码：550 / 561
页数：12
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