ArticleComputational and mathematical methods in medicine2023
Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.
Article in Computational and mathematical methods in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 21 citations in OpenAlex.
- AI-Driven Analysis of the Fetal Left Ventricular Outflow Tract: Diagnostic Value and Applications.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Emerging applications of artificial intelligence for obstetric ultrasound: A scoping review.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Current status and prospects of prenatal ultrasound diagnosis of congenital heart disease in fetuses: a narrative review.Translational pediatrics · 2026Review
- Automated and accurate view classification of fetal echocardiography using convolutional neural networks.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence based automatic classification, annotation, and measurement of the fetal heart using HeartAssist.Scientific reports · 2025Article
- The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review.Medicina (Kaunas, Lithuania) · 2025Review
- Complementary role of echocardiography, karyotyping, and chromosomal microarray in congenital cardiac anomalies.Frontiers in medicine · 2025Article
- Three-Dimensional Ultrasound for Physical and Virtual Fetal Heart Models: Current Status and Future Perspectives.Journal of clinical medicine · 2024Review
- Evolution of Fetal Cardiac Imaging over the Last 20 Years.Diagnostics (Basel, Switzerland) · 2023Review
- E-Health: A Game Changer in Fetal and Neonatal Cardiology?Journal of clinical medicine · 2023Review
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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