Diagnosis of fetal total anomalous pulmonary venous connection based on the post-left atrium space ratio using artificial intelligence
Abstract
OBJECTIVE: To explore whether the post-left atrium space (PLAS) ratio would be useful for prenatal diagnosis of total anomalous pulmonary venous connection (TAPVC) using echocardiography and artificial intelligence. METHODS: We retrospectively included 642 frames of four-chamber views from 319 fetuses (32 with TAPVC and 287 without TAPVC) in end-systolic and end-diastolic periods with multiple apex directions. The average gestational age was 25.6 ± 2.7 weeks. No other cardiac or extracardiac malformations were observed. The dataset was divided into a training set (n = 540; 48 with TAPVC and 492 without TAPVC) and test set (n = 102; 20 with TAPVC and 82 without TAPVC). The PLAS ratio was defined as the ratio of the epicardium-descending aortic distance to the center of the heart-descending aortic distance. Supervised learning was used in DeepLabv3+, FastFCN, PSPNet, and DenseASPP segmentation models. The area under the curve (AUC) was used on the test set. RESULTS: Expert annotations showed that this ratio was not related to the period or apex direction. It was higher in the TAPVC group than in the control group detected by the expert and the four models. The AUC of expert annotations, DeepLabv3+, FastFCN, PSPNet, and DenseASPP were 0.977, 0.941, 0.925, 0.856, and 0.887, respectively. CONCLUSION: Segmentation models achieve good diagnostic accuracy for TAPVC based on the PLAS ratio.
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Most fetal echocardiography studies only used 4CV images as the input for their algorithms.
“First, most of the studies only used 4CV images as the input for their algorithms [55,57,61,62,63,67,70]”
CHD was effectively detected by AI models generated by Gong et al. and Nurmaini et al., and selectively for total anomalous pulmonary venous connection by Wang et al.
“CHD was effectively detected by AI models generated by the research groups of Gong et al. and Nurmaini et al. [61,62], and selectively for the diagnosis of total anomalous pulmonary venous connection by Wang et al. [63].”
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- Authors
- Wang, X., Yang, T., Zhang, Y., Liu, X., Zhang, Y., Sun, L., Gu, X., Chen, Z., Guo, Y., Xue, C., Han, J., Zhu, H., & He, Y.
- Affiliations
- Capital Medical University (CN)
- State Key Laboratory of Software Development Environment Beihang University Beijing China
- Beijing Advanced Innovation Center for Big Data‐Based Precision Medicine Beihang University Beijing China
- Echocardiography Medical Center Beijing Anzhen Hospital Capital Medical University Beijing China
- Venue
- Prenatal Diagnosis
- Publisher
- Wiley
- Published
- 2022-08-19
- Volume
- 42
- Issue
- 10
- Pages
- 1323-1331
- ISSN
- 0197-3851 (ISSN-L)
- 1097-0223
- OpenAlex
- W4292374064
- Cited by
- 17
- References
- 41
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 2 · 2023: 4
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Prenatal Diagnosis (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (No. 82170301, and No. 82100322)
- Topics
- Health Sciences › Medicine › Epidemiology › Congenital Heart Disease Studies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- total anomalous pulmonary venous connection, fetal echocardiography, left atrium, deep learning segmentation, four-chamber view
- MeSH terms
- Artificial Intelligence, Female, Fetus, Heart Atria, Humans, Infant, Pregnancy, Pulmonary Veins, Retrospective Studies, Scimitar Syndrome, Ultrasonography, Prenatal
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