Artificial intelligence in prenatal ultrasound: A systematic review of diagnostic tools for detecting congenital anomalies
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
Artificial intelligence (AI) has potentially shown promise in interpreting ultrasound imaging through flexible pattern recognition and algorithmic learning, but implementation in clinical practice remains limited. This study aimed to investigate the current application of AI in prenatal ultrasounds to identify congenital anomalies, and to synthesise challenges and opportunities for the advancement of AI-assisted ultrasound diagnosis. This comprehensive analysis addresses the clinical translation gap between AI performance metrics and practical implementation in prenatal care.
Methods
Systematic searches were conducted in eight electronic databases (CINAHL Plus, Ovid/EMBASE, Ovid/MEDLINE, ProQuest, PubMed, Scopus, Web of Science and Cochrane Library) and Google Scholar from inception to May 2025. Studies were included if they applied an AI-assisted ultrasound diagnostic tool to identify a congenital anomaly during pregnancy. This review adhered to PRISMA guidelines for systematic reviews. We evaluated study quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines.
Findings
Of 9,918 records, 224 were identified for full-text review and 20 met the inclusion criteria. The majority of studies (11/20, 55%) were conducted in China, with most published after 2020 (16/20, 80%). All AI models were developed as an assistive tool for anomaly detection or classification. Most models (85%) focused on single-organ systems: heart (35%), brain/cranial (30%), or facial features (20%), while three studies (15%) attempted multi-organ anomaly detection. Fifty percent of the included studies reported exceptionally high model performance, with both sensitivity and specificity exceeding 0.95, with AUC-ROC values ranging from 0.91 to 0.97. Most studies (75%) lacked external validation, with internal validation often limited to small training and testing datasets.
Interpretation
While AI applications in prenatal ultrasound showed potential, current evidence indicates significant limitations in their practical implementation. Much work is required to optimise their application, including the external validation of diagnostic models with clinical utility to have real-world implications. Future research should prioritise larger-scale multi-centre studies, developing multi-organ anomaly detection capabilities rather than the current single-organ focus, and robust evaluation of AI tools in real-world clinical settings.
