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Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning
Open accessAbstract
Congenital heart disease (CHD) is the most common birth defect. Fetal survey ultrasound is recommended worldwide, including five views of the heart that together could detect 90% of complex CHD. In practice, however, sensitivity is as low as 30%. We hypothesized poor detection results from challenges in acquiring and interpreting diagnostic-quality cardiac views, and that deep learning could improve complex CHD detection. Using 107,823 images from 1,326 retrospective echocardiograms and surveys from 18-24 week fetuses, we trained an ensemble of neural networks to (i) identify recommended cardiac views and (ii) distinguish between normal hearts and complex CHD. Finally, (iii) we used segmentation models to calculate standard fetal cardiothoracic measurements. In a test set of 4,108 fetal surveys (0.9% CHD, >4.4 million images, about 400 times the size of the training dataset) the model achieved an AUC of 0.99, 95% sensitivity (95%CI, 84-99), 96% specificity (95%CI, 95-97), and 100% NPV in distinguishing normal from abnormal hearts. Sensitivity was comparable to clinicians’ task-for-task and remained robust on external and lower-quality images. The model’s decisions were based on clinically relevant features. Cardiac measurements correlated with reported measures for normal and abnormal hearts. Applied to guidelines-recommended imaging, ensemble learning models could significantly improve detection of fetal CHD and expand telehealth options for prenatal care at a time when the COVID-19 pandemic has further limited patient access to trained providers. This is the first use of deep learning to ∼double standard clinical performance on a critical and global diagnostic challenge.
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TL;DR
An ensemble of deep learning models was trained on fetal echocardiograms and screening ultrasounds to identify recommended cardiac views, distinguish normal hearts from complex congenital heart disease (CHD), and derive cardiothoracic measurements. In a large internal screening-ultrasound test set with population-like CHD prevalence, the model achieved an AUC of 0.99 with 95% sensitivity and 96% specificity.
Key findings
- Using up to 107,823 images from up to 1,326 studies, the ensemble achieved an AUC of 0.99 in OB-4000, a test set of 4,108 fetal surveys with 0.9% CHD and >4.4 million images; sensitivity was 95% (95% CI, 84–99%), specificity 96% (95% CI, 95–97%), positive predictive value 20% (95% CI, 17–23%), and negative predictive value 100%.
- View classification on FETAL-125 had an F score of 0.93 (AUC range, 0.94–0.98); on OB-125, sensitivity was 90% and specificity 78%, rising to 96% and 92% when using images with prediction probabilities at or above the first quartile.
- On external BCH-400 fetal echocardiograms with 92% CHD, composite classification AUC was 0.89; on TWINS-10, sensitivity was 100% and specificity 72%; for specific lesions, sensitivity was 71% for tetralogy of Fallot and 89% for hypoplastic left heart syndrome.
- Segmentation-derived measurements included cardiothoracic ratio, cardiac axis, and fractional area change; for example, mean cardiac axis was increased in tetralogy of Fallot at 63° ± 16° (range, 54–80°; P = 0.007) versus a normal reference of 45° ± 20°.
Methods
- Retrospective, de-identified fetal echocardiograms and second-trimester screening ultrasounds from 18- to 24-week fetuses, performed between 2000 and 2019 at UCSF and Boston Children’s Hospital; fetuses with clinically significant non-cardiac malformations were excluded, and gold-standard normal/CHD labels came from expert review plus electronic health-record codes for studies from 2012 onward.
- The ensemble used a ResNet-based view classifier for five recommended views (3VT, 3VV, LVOT, A4C, ABDO) plus non-target images, per-view binary diagnostic classifiers, a rule-based composite diagnostic classifier, and modified U-Net segmentation models for cardiothoracic structures.
- Evaluation used five independent test sets—FETAL-125, OB-125, OB-4000, BCH-400, and TWINS-10—with metrics including AUC, sensitivity, specificity, predictive values, F score, and Jaccard similarity; model performance was also compared with seven clinicians on a five-image-per-heart task in OB-125.
Limitations
- The paper states that testing was retrospective, that OB-4000 came from a center with fetal screening experience, and that prospective testing on consecutive cases across multiple centers—including rural, community, and non-expert centers—is needed, along with more multiple gestations and imaging with non-cardiac malformations.
- The paper also states that more training data are needed for specific lesion types and segmentation, and that further clinical validation of segmentation-derived fetal biometrics is required, especially for CHD lesions without established metrics.
- Evident from the text: the external BCH-400 test set had a very high CHD prevalence (92%) and lower composite AUC (0.89); the clinician comparison was limited to the 38 of 125 OB-125 surveys containing all five views; and data are not publicly available, with model weights available only upon request.
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In the citing paper
“Arnaout et al. used trained neural networks to detect the various cardiac views and distinguish between normal and abnormal images on fetal echocardiogram.77 The authors found an AUC of 0.99, a sensitivity of 95%, and specificity of 96%.”
From Artificial intelligence in obstetric ultrasound: A scoping review. (2023) · cites this paper as [77]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The combination of standard views allows for the detection of up to 90% of CHD; however, in clinical practice, the detection rate is only about 30% [48,50].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [50]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“summarized that AI applications rather assist than replace human observers [50].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [50]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The retrospective study of Huo et al. showed that AI-assisted US improved the accuracy of uterine fibroid assessment of young sonographers”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [50]
Experimental — accuracy not yet measuredIn the citing paper
“The results obtained following previous research using the DL method from fetal US images can help increase the detection of fetal heart abnormalities compared to human experts [10,17,18,24].”
From Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases (2022) · cites this paper as [24]
In the citing paper
“Worldwide, expert fetal cardiologists can detect as few as 30% to 50% of these CHD conditions before birth [24].”
From Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases (2022) · cites this paper as [24]
In the citing paper
“However, combining human-performed US and machine analysis allowed researchers to detect 95% CHDs in their test dataset.”
From Deep Learning for Improving the Effectiveness of Routine Prenatal Screening for Major Congenital Heart Diseases (2022) · cites this paper as [24]
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Different lower confidence limits for OB-4000 sensitivity
MinorThe abstract and Results report the same 95% sensitivity and 96% specificity for the 4,108-survey test set, but give different lower limits for the sensitivity confidence interval: 84% and 83%.
“In an internal test set of 4,108 fetal surveys (0.9% CHD, >4.4 million images), the model achieved an area under the curve (AUC) of 0.99, 95% sensitivity (95% confidence interval (CI), 84–99%), 96% specificity (95% CI, 95–97%)”
“This allowed a sensitivity of 95% (95% CI, 83–99%), a specificity of 96% (95% CI, 95–97%), a positive predictive value of 20% (95% CI, 17–23%) and a negative predictive value of 100% in OB-4000.”
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“To validate that the model generalizes beyond the medical center where it was trained 34 , we tested it on fetal echocardiograms from an unaffiliated, geographically remote medical center with a high prevalence of CHD (BCH-400; Table 1).”
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Metadata
- Authors
- Arnaout, R., Curran, L., Zhao, Y., Levine, J. C., Chinn, E., & Moon-Grady, A. J.
- Affiliations
- University of California, San Francisco (US)
- Boston Children's Hospital (US)
- Corresponding author
- Arnaout, Rima
- Venue
- Nature Medicine
- Publisher
- openRxiv
- Published
- 2020-06-24
- Volume
- 27
- Issue
- 5
- Pages
- 882-891
- ISSN
- 1078-8956 (ISSN-L)
- PMC
- PMC8380434
- OpenAlex
- W3161999894
- Cited by
- 315
- References
- 51
- Citations by year
- 2026: 55 · 2025: 61 · 2024: 66 · 2023: 60 · 2022: 45 · 2021: 26 · 2020: 2
- FWCI
- 14.24
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Medicine (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Defense (W81XWH1910294)
- U.S. Department of Health and Human Services (R01HL150394)
- American Heart Association (17IGMV33870001)
- National Institutes of Health (TR991872, UL1 TR991872)
- University of California, San Francisco
- National Heart, Lung, and Blood Institute (R01HL150394)
- National Center for Advancing Translational Sciences (UL1 TR991872)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Congenital Heart Disease Studies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- complex congenital heart disease, prenatal diagnosis, fetal echocardiography, fetal ultrasound screening, neural network ensembles, sensitivity and specificity
- MeSH terms
- Adult, Biometry, Female, Fetus, Heart, Heart Defects, Congenital, Humans, Mass Screening, Myocardium, Pregnancy, Pregnancy Trimester, Second, Prenatal Diagnosis, Sensitivity and Specificity, Thorax, Ultrasonography, Prenatal, Neural Networks, Computer, Echocardiography, Three-Dimensional, Young Adult
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