SynthesisFrontiers in cardiovascular medicine2025
Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: a systematic review and meta-analysis.
Synthesis in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Quantitative Ultrasound Imaging and Artificial Intelligence in Neonatal Echocardiography: Methodological Advances, Reproducibility Challenges, and Computational Perspectives.Journal of imaging · 2026Review
- Federated clinical concept and disease semantic learning for congenital heart disease diagnosis.NPJ digital medicine · 2026Article
- An interpretable deep learning model for first-trimester fetal cardiac screening.NPJ digital medicine · 2025Article
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Abstract
Background: Congenital heart disease (CHD) is a major contributor to morbidity and infant mortality and imposes the highest burden on global healthcare costs. Early diagnosis and prompt treatment of CHD contribute to enhanced neonatal outcomes and survival rates; however, there is a shortage of proficient examiners in remote regions. Artificial intelligence (AI)-powered ultrasound provides a potential solution to improve the diagnostic accuracy of fetal CHD screening. Methods: A literature search was conducted across seven databases for systematic review. Articles were retrieved based on PRISMA Flow 2020 and inclusion and exclusion criteria. Eligible diagnostic data were further meta-analyzed, and the risk of bias was tested using Quality Assessment of Diagnostic Accuracy Studies-Artificial Intelligence. Findings: A total of 374 studies were screened for eligibility, but only 9 studies were included. Most studies utilized deep learning models using either ultrasound or echocardiographic images. Overall, the AI models performed exceptionally well in accurately identifying normal and abnormal ultrasound images. A meta-analysis of these nine studies on CHD diagnosis resulted in a pooled sensitivity of 0.89 (0.81-0.94), a specificity of 0.91 (0.87-0.94), and an area under the curve of 0.952 using a random-effects model. Conclusion: Although several limitations must be addressed before AI models can be implemented in clinical practice, AI has shown promising results in CHD diagnosis. Nevertheless, prospective studies with bigger datasets and more inclusive populations are needed to compare AI algorithms to conventional methods. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461738, PROSPERO (CRD42023461738).
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