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

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 pr...

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Autores principales: Wu, Huiling, Wu, Bingzheng, Lai, Fangping, Liu, Peizhong, Lyu, Guorong, He, Shaozheng, Dai, Jiangfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889146/
https://www.ncbi.nlm.nih.gov/pubmed/36733613
http://dx.doi.org/10.1155/2023/5650378
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author Wu, Huiling
Wu, Bingzheng
Lai, Fangping
Liu, Peizhong
Lyu, Guorong
He, Shaozheng
Dai, Jiangfeng
author_facet Wu, Huiling
Wu, Bingzheng
Lai, Fangping
Liu, Peizhong
Lyu, Guorong
He, Shaozheng
Dai, Jiangfeng
author_sort Wu, Huiling
collection PubMed
description 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.
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spelling pubmed-98891462023-02-01 Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart Wu, Huiling Wu, Bingzheng Lai, Fangping Liu, Peizhong Lyu, Guorong He, Shaozheng Dai, Jiangfeng Comput Math Methods Med Research Article 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. Hindawi 2023-01-24 /pmc/articles/PMC9889146/ /pubmed/36733613 http://dx.doi.org/10.1155/2023/5650378 Text en Copyright © 2023 Huiling Wu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Huiling
Wu, Bingzheng
Lai, Fangping
Liu, Peizhong
Lyu, Guorong
He, Shaozheng
Dai, Jiangfeng
Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart
title Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart
title_full Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart
title_fullStr Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart
title_full_unstemmed Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart
title_short Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart
title_sort application of artificial intelligence in anatomical structure recognition of standard section of fetal heart
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889146/
https://www.ncbi.nlm.nih.gov/pubmed/36733613
http://dx.doi.org/10.1155/2023/5650378
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