ReviewFrontiers in cardiovascular medicine2024
Applications of artificial intelligence-powered prenatal diagnosis for congenital heart disease.
Review in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- Heartbeat: a multimodal dataset of fetal echocardiography and clinical metadata for early detection of congenital heart disease.Frontiers in cardiovascular medicine · 2026Article
- Fetal dextro-transposition of the great arteries: a narrative review of management and neurodevelopmental outcomes.Frontiers in pharmacology · 2026Review
- Tailored Therapeutic Strategies for Fetuses, Neonates, Pediatrics, Geriatrics, Athletes, and Critical Cases in the Era of Personalized Medicine.Diseases (Basel, Switzerland) · 2025Review
- Article
- Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.Journal of clinical ultrasound : JCU · 2025Review
- Enhancing Prenatal Diagnosis of Obstructive Aortic Arch Abnormalities: Evaluating the Role of the Longitudinal Visualization Techniques.Journal of medical ultrasoundArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has made significant progress in the medical field in the last decade. The AI-powered analysis methods of medical images and clinical records can now match the abilities of clinical physicians. Due to the challenges posed by the unique group of fetuses and the dynamic organ of the heart, research into the application of AI in the prenatal diagnosis of congenital heart disease (CHD) is particularly active. In this review, we discuss the clinical questions and research methods involved in using AI to address prenatal diagnosis of CHD, including imaging, genetic diagnosis, and risk prediction. Representative examples are provided for each method discussed. Finally, we discuss the current limitations of AI in prenatal diagnosis of CHD, namely Volatility, Insufficiency and Independence (VII), and propose possible solutions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.