Artificial intelligence in perinatal diagnosis and management of congenital heart disease
Abstract
Prenatal diagnosis and management of congenital heart disease (CHD) has progressed substantially in the past few decades. Fetal echocardiography can accurately detect and diagnose approximately 85% of cardiac anomalies. The prenatal diagnosis of CHD results in improved care, with improved risk stratification, perioperative status and survival. However, there is much work to be done. A minority of CHD is actually identified prenatally. This seemingly incongruous gap is due, in part, to diminished recognition of an anomaly even when present in the images and the need for increased training to obtain specialized cardiac views. Artificial intelligence (AI) is a field within computer science that focuses on the development of algorithms that "learn, reason, and self-correct" in a human-like fashion. When applied to fetal echocardiography, AI has the potential to improve image acquisition, image optimization, automated measurements, identification of outliers, classification of diagnoses, and prediction of outcomes. Adoption of AI in the field has been thus far limited by a paucity of data, limited resources to implement new technologies, and legal and ethical concerns. Despite these barriers, recognition of the potential benefits will push us to a future in which AI will become a routine part of clinical practice.
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US is time consuming and can require substantial training and experience for specific indications such as fetal echocardiography or neurosonography.
“In clinical settings, US is known to be time consuming and can require a substantial amount of training and experience for specific indications, such as fetal echocardiography or neurosonography [5,6,7].”
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- Authors
- Reddy, C. D., Van den Eynde, J., & Kutty, S.
- Affiliations
- Stanford Medicine (US)
- Johns Hopkins University (US)
- Corresponding author
- Reddy, Charitha D.
- Venue
- Seminars in Perinatology
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 46
- Issue
- 4
- Pages
- 151588
- ISSN
- 0146-0005 (ISSN-L)
- OpenAlex
- W4220766044
- Semantic Scholar
- 1c363ae679654b7d17f5282d9cb7a1b91813e73d
- Cited by
- 32
- References
- 63
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 11 · 2023: 8
- FWCI
- 4.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Seminars in Perinatology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Congenital Heart Disease Studies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, congenital heart disease, prenatal diagnosis, fetal echocardiography, risk stratification, outcome prediction, data scarcity
- MeSH terms
- Algorithms, Artificial Intelligence, Echocardiography, Female, Heart Defects, Congenital, Humans, Pregnancy, Prenatal Diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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