Evidence map›Paper›PMID 41031567›Full record

ArticleHuman brain mapping2025

Cross-Modality Comparison of Fetal Brain Phenotypes: Insights From Short-Interval Second-Trimester MRI and Ultrasound Imaging.

Madeleine K Wyburd, Nicola K Dinsdale, Vanessa Kyriakopoulou, Lorenzo Venturini, Robert Wright, Alena Uus, Jacqueline Matthew, Emily Skelton, Lilla Zöllei, Joseph Hajnal and 1 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

11 authors.

Madeleine K WyburdOxford Machine Learning in NeuroImaging Lab, Department of Computer Science, University of Oxford, Oxford, UK.ORCID 0000-0003-0991-3204
Nicola K DinsdaleOxford Machine Learning in NeuroImaging Lab, Department of Computer Science, University of Oxford, Oxford, UK.
Vanessa KyriakopoulouResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Lorenzo VenturiniResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Robert WrightResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Alena UusResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Jacqueline MatthewResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Emily SkeltonSchool of Health and Psychological Sciences, City, University of London, London, UK.
Lilla ZölleiAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID 0000-0002-6447-9626
Joseph HajnalResearch Department of Imaging Physics and Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Ana I L NambureteOxford Machine Learning in NeuroImaging Lab, Department of Computer Science, University of Oxford, Oxford, UK.

Funding

National Institute for Health Research (NIHR) Clinical Research Facility based at Guy's and St Thomas' NHS Foundation Trust and King's College LondonThe Bill and Melinda Gates Foundation INV-044645Wellcome/EPSRC Centre for Medical Engineering WT203148/Z/16/ZWellcome TrustWellcome Trust 102431
6 · The paper itself

Abstract

Advances in fetal three-dimensional (3D) ultrasound (US) and magnetic resonance imaging (MRI) have revolutionized the study of fetal brain development, enabling detailed analysis of brain structures and growth. Despite their complementary capabilities, these modalities capture fundamentally different physical signals, potentially leading to systematic differences in image-derived phenotypes (IDPs). Here, we evaluate the agreement of IDPs between US and MRI by comparing the volumes of eight brain structures from 90 subjects derived using deep-learning algorithms from majority same-day imaging (days between scans: mean = 1.2, mode = 0 and max = 4). Excellent agreement (intra-class correlation coefficient,

Indexed as

BrainMagnetic Resonance ImagingNeuroimagingUltrasonography, PrenatalAdultDeep LearningFemaleFetusHumansImaging, Three-DimensionalPhenotypePregnancyPregnancy Trimester, Second

Identifiers

PMID41031567
PMCPMC12485670

What OpenQuestion holds

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LicenceCC BY
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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.