SynthesisFetal diagnosis and therapy2024
Artificial Intelligence in Imaging in the First Trimester of Pregnancy: A Systematic Review.
Synthesis in Fetal diagnosis and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 4 citations in OpenAlex.
- The quintessential high-risk profile: advanced management strategies for placenta accreta spectrum in patients with advanced maternal age and IVF conception.BMC pregnancy and childbirth · 2026Review
- Deep Learning-Based Segmentation of Fetal Anatomical Structures in the First Trimester.Prenatal diagnosis · 2026Article
- Quality evaluation of ultrasound images for fetal crown-rump length measurement at 11 to 14 weeks' gestation: A fusion model can be interpreted using SHAP method.The ultrasound journal · 2026Article
- Diagnostic and Therapeutic Challenges in Rare and Non-Tubal Ectopic Pregnancies: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Intelligent quality assessment of ultrasound images for fetal nuchal translucency measurement during the first trimester of pregnancy based on deep learning models.BMC pregnancy and childbirth · 2025Article
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionUltrasonography 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.
objectiveThe objective of the study was to conduct a systematic review of the use of AI in imaging in the first trimester of pregnancy.
methodsWe 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.
resultsOf 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.
conclusionEven 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.