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胎儿超声心动图中的人工智能:最新进展与未来展望。

Artificial intelligence in fetal echocardiography: Recent advances and future prospects.

作者信息

Ma Mingming, Sun Li-Hua, Chen Ran, Zhu Jiang, Zhao Bowen

机构信息

Department of Diagnostic Ultrasound & Echocardiography, Sir Run Run Shaw Hospital, Zhejiang University College of Medicine, Technical Guidance Center for Fetal Echocardiography of Zhejiang Province & Sir Run Run Shaw Institute of Clinical Medicine of Zhejiang University, Hangzhou 310016, China.

Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapyfor Major Gynecological Diseases, Women's Hospital of Zhejiang University School of Medicine, Hangzhou 310000, China.

出版信息

Int J Cardiol Heart Vasc. 2024 Jul 27;53:101380. doi: 10.1016/j.ijcha.2024.101380. eCollection 2024 Aug.

Abstract

In the past few decades, great progress has been made in prenatal diagnosis of congenital heart disease (CHD). Fetal echocardiography is recognized as the main prenatal screening and diagnostic tool that can accurately detect approximately 85 % of fetal cardiac abnormalities. Evaluation of the fetal heart remains a major challenge in prenatal ultrasound screening and diagnosis due to fetal position, involuntary movement, small and complex fetal cardiac anatomy, maternal abdominal wall conditions, and lack of expertise in fetal echocardiography by some physicians engaged in obstetric ultrasound. Artificial intelligence (AI) can automate and standardize the display of each diagnostic section of the fetal heart and thus contribute to accurate diagnosis, which significantly optimizes the clinical application of fetal echocardiography. In this review, we not only clarify the role of AI but also highlight its significance and future solutions in the field of fetal echocardiography.

摘要

在过去几十年中,先天性心脏病(CHD)的产前诊断取得了巨大进展。胎儿超声心动图被公认为主要的产前筛查和诊断工具,能够准确检测出约85%的胎儿心脏异常。由于胎儿体位、不自主运动、胎儿心脏解剖结构小且复杂、孕妇腹壁状况以及一些从事产科超声检查的医生缺乏胎儿超声心动图专业知识,胎儿心脏评估仍是产前超声筛查和诊断中的一项重大挑战。人工智能(AI)可以使胎儿心脏各诊断切面的显示自动化和标准化,从而有助于准确诊断,这显著优化了胎儿超声心动图的临床应用。在本综述中,我们不仅阐明了AI的作用,还强调了其在胎儿超声心动图领域的意义和未来解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/976f/11327943/3fd6bad0c27d/gr1.jpg

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