ArticleNPJ digital medicine2023
A multicenter study on two-stage transfer learning model for duct-dependent CHDs screening in fetal echocardiography.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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.Frontiers in cardiovascular medicine · 2025Pooled it
- 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
- Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.Nature communications · 2026Article
- Current status and prospects of prenatal ultrasound diagnosis of congenital heart disease in fetuses: a narrative review.Translational pediatrics · 2026Review
- TLEUDS: a cascade Dual-Transfer learning system with quality- and knowledge-enhanced for precise fetal CHD screening.BioData mining · 2026Article
- Automated interpretation of fetal cardiac function evaluation from the echocardiogram.NPJ digital medicine · 2026Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Resolving Early Targets and Metabolomic Profile of Congenital Heart Disease Through Tandem Mass Spectrometry Screening in Neonates.Journal of the American Heart Association · 2025Article
- Keeping the Ductus Arteriosus Patent: Current Strategy and Perspectives.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Duct-dependent congenital heart diseases (CHDs) are a serious form of CHD with a low detection rate, especially in underdeveloped countries and areas. Although existing studies have developed models for fetal heart structure identification, there is a lack of comprehensive evaluation of the long axis of the aorta. In this study, a total of 6698 images and 48 videos are collected to develop and test a two-stage deep transfer learning model named DDCHD-DenseNet for screening critical duct-dependent CHDs. The model achieves a sensitivity of 0.973, 0.843, 0.769, and 0.759, and a specificity of 0.985, 0.967, 0.956, and 0.759, respectively, on the four multicenter test sets. It is expected to be employed as a potential automatic screening tool for hierarchical care and computer-aided diagnosis. Our two-stage strategy effectively improves the robustness of the model and can be extended to screen for other fetal heart development defects.
Identifiers
What OpenQuestion holds
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.