Artificial Intelligence in Imaging in the First Trimester of Pregnancy: A Systematic Review.

Ultrasonography in the first trimester of pregnancy offers an early screening tool to identify high risk pregnancies. Artificial intelligence (AI) algorithms have the potential to improve the accuracy of diagnosis and assist the clinician in early risk stratification. The objective of the study was to conduct a systematic review of the use of AI in imaging in the first trimester of pregnancy. We conducted a systematic literature review by searching in computerized databases PubMed, Embase, and Google Scholar from inception to January 2024. Full-text peer-reviewed journal publications written in English on the evaluation of AI in first-trimester pregnancy imaging were included. Review papers, conference abstracts, posters, animal studies, non-English and non-peer-reviewed articles were excluded. Risk of bias was assessed by using PROBAST. Of the 1,595 non-duplicated records screened, 27 studies were included. Twelve studies focussed on segmentation, 8 on plane detection, 6 on image classification, and one on both segmentation and classification. Five studies included fetuses with a gestational age of less than 10 weeks. The size of the datasets was relatively small as 16 studies included less than 1,000 cases. The models were evaluated by different metrics. Duration to run the algorithm was reported in 12 publications and ranged between less than one second and 14 min. Only one study was externally validated. Even though the included algorithms reported a good performance in a research setting on testing datasets, further research and collaboration between AI experts and clinicians is needed before implementation in clinical practice. © 2024 The Author(s). Published by S. Karger AG, Basel.

Citation

Emma Umans, Kobe Dewilde, Helena Williams, Jan Deprest, Thierry Van den Bosch. Artificial Intelligence in Imaging in the First Trimester of Pregnancy: A Systematic Review. Fetal diagnosis and therapy. 2024;51(4):343-356

Mesh Tags

PMID: 38493764

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