Publication Date
12-25-2024
Journal
Children
DOI
10.3390/children12010014
PMID
39857845
PMCID
PMC11764430
PubMedCentral® Posted Date
12-25-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
artificial intelligence, pediatric echocardiography, fetal echocardiography, congenital heart disease, machine learning, deep learning
Abstract
Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI into practice. This review highlights AI's current applications, challenges, and future directions in fetal and pediatric echocardiography.
Included in
Artificial Intelligence and Robotics Commons, Cardiology Commons, Cardiovascular Diseases Commons, Congenital, Hereditary, and Neonatal Diseases and Abnormalities Commons, Medical Sciences Commons, Pediatrics Commons