Evidence map›Paper›PMID 39916306›Full record

ArticlePrenatal diagnosis2025

Video Clip Extraction From Fetal Ultrasound Scans Using Artificial Intelligence to Allow Remote Second Expert Review for Congenital Heart Disease.

Thomas G Day, Lorenzo Venturini, Samuel F Budd, Alfonso Farruggia, Robert Wright, Jackie Matthew, Vita Zidere, Trisha Vigneswaran, Ilaria Bo, Alex Savis and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Prenatal diagnosis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Thomas G DaySchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0001-8391-7903
Lorenzo VenturiniSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Samuel F BuddSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Alfonso FarruggiaSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Robert WrightSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Jackie MatthewSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Vita ZidereFetal Cardiology Unit, Department of Congenital Heart Disease, Evelina London Children's Healthcare, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Trisha VigneswaranFetal Cardiology Unit, Department of Congenital Heart Disease, Evelina London Children's Healthcare, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Ilaria BoFetal Cardiology Unit, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Alex SavisFetal Cardiology Unit, Department of Congenital Heart Disease, Evelina London Children's Healthcare, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Jo WolfendenFetal Cardiology Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
John SimpsonSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Jo HajnalSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Bernhard KainzSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Reza RazaviSchool of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Funding

King's Health PartnersNational Institute for Health and Care Research NIHR301448Wellcome TrustWellcome Trust WT220160/Z/20/Z
6 · The paper itself

Abstract

objectiveTo use artificial intelligence (AI) to automatically extract video clips of the fetal heart from a stream of ultrasound video, and to assess the performance of these when used for remote second review.

methodsUsing a dataset from a previous clinical trial of AI to assist in fetal ultrasound scanning, AI was used to automatically extract video clips of the fetal heart from ultrasound scans of 48 fetuses in which the diagnosis was known: 24 normal and 24 with congenital heart disease (CHD). These, and manually still saved images, were shown in a random order to expert clinicians, who were asked to detect cardiac abnormalities.

resultsThe initial manual scan had a sensitivity of 0.792 and specificity of 0.917 for detecting CHD in this cohort. The addition of second review improved the sensitivity to 0.975 using video clips, which was significantly higher than using still images (0.892, p = 0.002). There was a significant drop in specificity to 0.767 and 0.833 (p < 0.001) for the video and still method, respectively, which were statistically similar to each other (p = 0.117). The median review time was 1.0 min (IQR 0.71) for the still images, and 3.75 min (IQR 3.12) for the AI-generated video clips.

conclusionAI can be used to automatically extract fetal cardiac video clips, and these can be used for remote second review to improve detection rates. Video clips are superior to still images, but both methods result in a significant drop in specificity.

Indexed as

Artificial IntelligenceFetal HeartHeart Defects, CongenitalUltrasonography, PrenatalVideo RecordingFemaleHumansPregnancySensitivity and Specificity

Identifiers

PMID39916306
PMCPMC11987776

What OpenQuestion holds

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Registered trials

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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.