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人工智能在胎儿心脏标准切面解剖结构识别中的应用。

Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.

机构信息

Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.

College of Engineering, Huaqiao University, Quanzhou 362021, China.

出版信息

Comput Math Methods Med. 2023 Jan 24;2023:5650378. doi: 10.1155/2023/5650378. eCollection 2023.

Abstract

Congenital heart defect (CHD) refers to the overall structural abnormality of the heart or large blood vessels in the chest cavity. It is the most common type of fetal congenital defects. Prenatal diagnosis of congenital heart disease can improve the prognosis of the fetus to a certain extent. At present, prenatal diagnosis of CHD mainly uses 2D ultrasound to directly evaluate the development and function of fetal heart and main structures in the second trimester of pregnancy. Artificial recognition of fetal heart 2D ultrasound is a highly complex and tedious task, which requires a long period of prenatal training and practical experience. Compared with manual scanning, computer automatic identification and classification can significantly save time, ensure efficiency, and improve the accuracy of diagnosis. In this paper, an effective artificial intelligence recognition model is established by combining ultrasound images with artificial intelligence technology to assist ultrasound doctors in prenatal ultrasound fetal heart standard section recognition. The method data in this paper were obtained from the Second Affiliated Hospital of Fujian Medical University. The fetal apical four-chamber heart section, three vessel catheter section, three vessel trachea section, right ventricular outflow tract section, and left ventricular outflow tract section were collected at 20-24 weeks of gestation. 2687 image data were used for model establishment, and 673 image data were used for model validation. The experiment shows that the map value of this method in identifying different anatomical structures reaches 94.30%, the average accuracy rate reaches 94.60%, the average recall rate reaches 91.0%, and the average F1 coefficient reaches 93.40%. The experimental results show that this method can effectively identify the anatomical structures of different fetal heart sections and judge the standard sections according to these anatomical structures, which can provide an auxiliary diagnostic basis for ultrasound doctors to scan and lay a solid foundation for the diagnosis of congenital heart disease.

摘要

先天性心脏病(CHD)是指胸腔内心脏或大血管的整体结构异常。它是胎儿先天性缺陷中最常见的类型。产前诊断先天性心脏病可以在一定程度上改善胎儿的预后。目前,先天性心脏病的产前诊断主要采用二维超声在妊娠中期直接评估胎儿心脏和主要结构的发育和功能。胎儿二维超声的人工识别是一项高度复杂和繁琐的任务,需要长期的产前培训和实践经验。与手动扫描相比,计算机自动识别和分类可以显著节省时间,提高效率,并提高诊断的准确性。本文结合人工智能技术,建立了一种有效的人工智能识别模型,以协助超声医生对产前超声胎儿心脏标准切面进行识别。本文方法的数据来自福建医科大学第二附属医院。在妊娠 20-24 周时,采集胎儿心尖四腔心切面、三血管导管切面、三血管气管切面、右心室流出道切面和左心室流出道切面。共采集 2687 幅图像数据用于模型建立,673 幅图像数据用于模型验证。实验表明,该方法在识别不同解剖结构的地图值达到 94.30%,平均准确率达到 94.60%,平均召回率达到 91.0%,平均 F1 系数达到 93.40%。实验结果表明,该方法能够有效识别不同胎儿心脏切面的解剖结构,并根据这些解剖结构判断标准切面,为超声医生扫描提供辅助诊断依据,为先天性心脏病的诊断奠定坚实基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc1f/9889146/8577cdf55d1b/CMMM2023-5650378.001.jpg

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