Deep learning for cardiac MRI: performance evidence and barriers to clinical integration. A Systematic Review and Meta-Analysis.

Fatemah Aladwani, Alessandro Perelli, Ify Mordi, Faisel Khan

Abstract read

In one paragraph

Article in European heart journal. Imaging methods and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Fatemah AladwaniDepartment of Cardiovascular Research, School of Medicine, University of Dundee, Dundee DD1 9SY, UK.ORCID https://orcid.org/0009-0007-4178-4162

Alessandro PerelliDivision of Biomedical Engineering, School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK.ORCID https://orcid.org/0000-0002-0511-2293

Ify MordiDepartment of Cardiovascular Research, School of Medicine, University of Dundee, Dundee DD1 9SY, UK.ORCID https://orcid.org/0000-0002-2686-729X

Faisel KhanDepartment of Cardiovascular Research, School of Medicine, University of Dundee, Dundee DD1 9SY, UK.ORCID https://orcid.org/0000-0002-9889-0229

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: This systematic review and meta-analysis aimed to evaluate the current evidence on the use of deep learning in cardiac magnetic resonance imaging, focusing on image segmentation, prediction, and diagnosis. Methods and results: A systematic search of Medline, Web of Science, Embase, and Scopus identified studies published between 2020 and 2025. Eligible studies comprised deep learning-based segmentation, prediction, or diagnosis of cardiac magnetic resonance images. MetaDisc version 1.4 was used for statistical analysis, with a Conclusion: Deep learning models show excellent performance in cardiac magnetic resonance segmentation and diagnosis, often matching or exceeding manual analysis, indicating strong potential for clinical adoption.This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration number CRD42023439659.

Indexed as

cardiovascular disease (CVD)CMR imagingdeep learning (DL)diagnosisimage segmentationprediction

Identifiers

PMID41877690

DOI10.1093/ehjimp/qyag045

PMCPMC13007597

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.