Application and Progress of Artificial Intelligence in Fetal Ultrasound

被引：23

作者：

Xiao, Sushan ^{[1
,2
,3
]}

Zhang, Junmin ^{[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
]}

机构：

[1] Huazhong Univ Sci & Technol, Union Hosp, Tongji Med Coll, Dept Ultrasound Med, Wuhan 430022, Peoples R China

[2] Clin Res Ctr Med Imaging Hubei Prov, Wuhan 430022, Peoples R China

[3] Hubei Prov Key Lab Mol Imaging, Wuhan 430022, Peoples R China

来源：

JOURNAL OF CLINICAL MEDICINE | 2023年 / 12卷 / 09期

基金：

中国国家自然科学基金;

关键词：

fetal ultrasound; artificial intelligence; prenatal diagnosis; deep learning; convolution neural network; NAVIGATION ECHOCARDIOGRAPHY FINE; CENTRAL-NERVOUS-SYSTEM; GESTATIONAL-AGE; SONOGRAPHIC EXAMINATION; GUIDELINES; IMAGES;

D O I：

10.3390/jcm12093298

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.

引用

页数：16

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