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

被引：7
作者：
Wu H. [1 ]
Wu B. [2 ]
Lai F. [1 ]
Liu P. [2 ]
Lyu G. [1 ,3 ]
He S. [1 ]
Dai J. [1 ]
机构：
[1] Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou
[2] College of Engineering, Huaqiao University, Quanzhou
[3] Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou
来源：
Computational and Mathematical Methods in Medicine | 2023年 / 2023卷
关键词：
Compendex;
D O I：
10.1155/2023/5650378
中图分类号：
学科分类号：
摘要：
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. © 2023 Huiling Wu et al.
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