Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart
Open accessAbstract
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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Advantages of AI in fetal echocardiography include facilitation of standard view acquisition and CHD detection, and significant reduction in examination time.
“Advantages are the facilitation of standard view acquisition [58,60] and CHD detection [53,61,62], as well as a significant reduction in examination time [52,55].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [58]
The Discussion (section 4.4; page unavailable) says the model identifies standard fetal-heart views faster and could help doctors obtain them. The Conclusion (section 5; page unavailable) describes potential assistance with CHD screening, and the Abstract says automatic identification can save time compared with manual scanning. The paper does not report a measured, statistically significant reduction in examination time or demonstrate improved CHD detection. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRecognition of small CHD or small anatomical structures such as the trachea was limited in some models.
“Lastly, the recognition of small CHD or small anatomical structures such as the trachea was limited in some models [55,58].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [58]
The Test Results section reports that trachea recognition was the weakest among the anatomical structures tested, and the Discussion says its recognition effect was relatively poor. The paper also discusses differences between models in recognizing small anatomical targets. It does not report model recognition results for small congenital heart defects, so that part of the claim is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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References 22 · Cited by 20
Research works printed but not linked: 3
- [3]Guorong L. Ultrasound Diagnosis of Fetal Brain and Cardiac Malformations 2010 Peking University Medical Press
- [9]Yuhuan H. Wendi T. Xin L. Yueqing W. Yu Y. Design and implementation of the standard section navigation visualization system for transesophageal echocardiographycomputer application Chinese Journal of Computer Application 2015 35 212-215 p. 241
- [25]Yan J. Yan X. Yang J. Analysis of the clinical significance and the risk factors of prenatal ultrasound screening for fetal congenital heart disease Chinese Journal of General Practice 2016 14 1 114 115
- Authors
- Wu, H., Wu, B., Lai, F., Liu, P., Lyu, G., He, S., & Dai, J.
- Affiliations
- Second Affiliated Hospital of Fujian Medical University (CN)
- Huaqiao University (CN)
- Corresponding author
- Lyu, Guorong
- He, Shaozheng
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2023-01
- Volume
- 2023
- Issue
- 1
- Pages
- 5650378-5650378
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- PMC
- PMC9889146
- OpenAlex
- W4317840203
- Cited by
- 20
- References
- 22
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 5 · 2023: 5
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Natural Science Foundation of Fujian Province (2021J01275, 2020J0112)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Epidemiology › Congenital Heart Disease Studies
- Keywords
- fetal heart, congenital heart disease, prenatal diagnosis, prenatal ultrasound, anatomical structure recognition, computer-aided diagnosis, apical four-chamber view, right ventricular outflow tract, left ventricular outflow tract, second trimester
- MeSH terms
- Artificial Intelligence, Echocardiography, Female, Fetal Heart, Heart Defects, Congenital, Humans, Pregnancy, Ultrasonography, Prenatal
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