Front Pediatr. 2025 Oct 8;13:1653305. doi: 10.3389/fped.2025.1653305. eCollection 2025.
ABSTRACT
PURPOSE: Congenital heart disease (CHD) is the most common birth defect and a leading cause of neonatal morbidity and mortality. Despite advances in prenatal imaging, rural communities face persistent disparities in CHD detection due to limited access to specialized diagnostics. This position paper proposes an AI-enabled framework to embed early CHD detection into routine prenatal care and reduce the rural-urban gap in Nebraska.
METHOD: A review of 1,502 surgical CHD cases at Children’s Nebraska (2019-2024) revealed significant geographic disparities in prenatal detection. In response, we outline a framework that leverages a secure, cloud-based platform to apply AI algorithms to standard obstetric ultrasound images. Flagged cases are referred to nearby fetal cardiology outreach centers, reducing delays associated with centralized tertiary care access.
FRAMEWORK: This approach leverages existing infrastructure, including the Children’s Nebraska fetal heart center, UNMC’s rural residency network, and maternal-fetal medicine collaborations. Implementation will be led by an interdisciplinary team spanning cardiology, Obstetrics, rural health, imaging, and machine learning.
CONCLUSION: By decentralizing diagnostics and enabling earlier triaging in community settings, this scalable, accessible framework offers a practical solution for improving prenatal CHD detection in underserved regions, with strong potential for national replication.
PMID:41132758 | PMC:PMC12541414 | DOI:10.3389/fped.2025.1653305
