Enhancing Fetal Cardiac Imaging With Artificial Intelligence

Congenital heart disease (CHD) is the most common major birth anomaly and a key cause of neonatal mortality. While early diagnosis improves outcomes, prenatal detection remains inconsistent. Artificial intelligence (AI) offers scalable solutions through automation of view acquisition, image interpretation, and functional assessment. AI has shown expert-level performance in view classification, CHD detection, and cardiac biometry. Tools like Fetal Intelligent Navigation Echocardiography, though not AI, enhance consistency and efficiency. Emerging AI modalities, including generative AI, self-supervised learning, and NLP-driven report automation, expand possibilities. Ongoing research is essential to ensure safe, equitable integration of AI into clinical workflows for improved CHD diagnosis worldwide.

Authors

Publication Details

Journal
Clinical Obstetrics & Gynecology
Published
2025-12-23
DOI
https://doi.org/10.1097/grf.0000000000000992
Primary Topic
Fetal and Pediatric Neurological Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Fetal Cardiac Imaging With Artificial Intelligence

Elena Sinkovskaya, Rebecca Horgan, Juliana G. Martins
Clinical Obstetrics & Gynecology
Fetal and Pediatric Neurological Disorders
article

Enhancing Fetal Cardiac Imaging With Artificial Intelligence

Elena Sinkovskaya, Rebecca Horgan, Juliana G. Martins
article en

Abstract

Congenital heart disease (CHD) is the most common major birth anomaly and a key cause of neonatal mortality. While early diagnosis improves outcomes, prenatal detection remains inconsistent. Artificial intelligence (AI) offers scalable solutions through automation of view acquisition, image interpretation, and functional assessment. AI has shown expert-level performance in view classification, CHD detection, and cardiac biometry. Tools like Fetal Intelligent Navigation Echocardiography, though not AI, enhance consistency and efficiency. Emerging AI modalities, including generative AI, self-supervised learning, and NLP-driven report automation, expand possibilities. Ongoing research is essential to ensure safe, equitable integration of AI into clinical workflows for improved CHD diagnosis worldwide.

Clinical Obstetrics & GynecologyVol. 69(1)
Good health and well-being
Openalex Percentile: Top 18%
Fetal and Pediatric Neurological Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.