ArticleScientific reports2024
AI supported fetal echocardiography with quality assessment.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bias in AI models for fetal cardiac screening: the consequences of training predominantly on normal anatomy.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Article
- The Three-Y and Trachea View (3Y + T): A Conceptual Extension of the Three-Vessel and Trachea View for Comprehensive Fetal Echocardiographic Assessment.Diagnostics (Basel, Switzerland) · 2026Review
- Assessment of Generative AI Chatbots for Neonatal Diagnostic Reasoning: A Comparative Concordance and Diagnosis Acceptability Analysis.Indian journal of pediatrics · 2026Article
- Fetal cardiac remodeling in second trimester in pregnancies with pre-eclampsia and/or fetal growth restriction: deep-learning-based approach using population-wide data.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Article
- AI-based quality control was associated with improved fetal ultrasound image quality in low-resource settings: a real-world multicenter study from West China.BMC medicine · 2026Observational
- How Should We Grade the Quality of a Transthoracic Echocardiogram: Results from a Survey from the Association for European Pediatric and Congenital Cardiology (AEPC) Imaging Working Group.Pediatric cardiology · 2026Article
- Development of a Novel Machine Learning Model for Automatic Assessment of Quality of Transvaginal Ultrasound Images From Multi-Annotator Labels.Australasian journal of ultrasound in medicine · 2025Article
- Deep learning-based no-reference quality assessment of anterior segment ultrasound biomicroscopy panoramic images.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence based automatic classification, annotation, and measurement of the fetal heart using HeartAssist.Scientific reports · 2025Article
- The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review.Medicina (Kaunas, Lithuania) · 2025Review
- Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility.Internal medicine (Tokyo, Japan) · 2025Review
- Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
This study aimed to develop a deep learning model to assess the quality of fetal echocardiography and to perform prospective clinical validation. The model was trained on data from the 18-22-week anomaly scan conducted in seven hospitals from 2008 to 2018. Prospective validation involved 100 patients from two hospitals. A total of 5363 images from 2551 pregnancies were used for training and validation. The model's segmentation accuracy depended on image quality measured by a quality score (QS). It achieved an overall average accuracy of 0.91 (SD 0.09) across the test set, with images having above-average QS scoring 0.97 (SD 0.03). During prospective validation of 192 images, clinicians rated 44.8% (SD 9.8) of images as equal in quality, 18.69% (SD 5.7) favoring auto-captured images and 36.51% (SD 9.0) preferring manually captured ones. Images with above average QS showed better agreement on segmentations (p < 0.001) and QS (p < 0.001) with fetal medicine experts. Auto-capture saved additional planes beyond protocol requirements, resulting in more comprehensive echocardiographies. Low QS had adverse effect on both model performance and clinician's agreement with model feedback. The findings highlight the importance of developing and evaluating AI models based on 'noisy' real-life data rather than pursuing the highest accuracy possible with retrospective academic-grade data.
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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.