Evidence map›Paper›PMID 42146868›Full record

ArticleEuropean heart journal. Digital health2026

Automated assessment of right ventricular systolic function from coronary angiograms with video-based artificial intelligence algorithms: development, validation, comparison against humans, and prospective deployment.

Fatima Zahra Fawzi, Istok Menkovic, Nicolas Dostie, Maxime Tremblay-Gravel, Marie-Claude Parent, Gabriel Asslo, Jean-François Tanguay, Guillaume Marquis-Gravel, Minhaj Ansari, Joshua P Barrios and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 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

13 authors.

Fatima Zahra FawziDivision of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, Montreal, QC H1T 1C8, Canada.
Istok MenkovicDepartment of Medicine, Université de Montréal, 2900 Edouard Montpetit Blvd, Montreal, Quebec H3T 1J4, Canada.
Nicolas DostieDepartment of Medicine, Université de Montréal, 2900 Edouard Montpetit Blvd, Montreal, Quebec H3T 1J4, Canada.
Maxime Tremblay-GravelDivision of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, Montreal, QC H1T 1C8, Canada.ORCID https://orcid.org/0000-0001-6586-2064
Marie-Claude ParentDivision of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, Montreal, QC H1T 1C8, Canada.ORCID https://orcid.org/0000-0001-7886-8115
Gabriel AssloDivision of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, Montreal, QC H1T 1C8, Canada.ORCID https://orcid.org/0009-0006-6581-5632
Jean-François TanguayDivision of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, Montreal, QC H1T 1C8, Canada.ORCID https://orcid.org/0000-0003-1057-0528
Guillaume Marquis-GravelDivision of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, Montreal, QC H1T 1C8, Canada.ORCID https://orcid.org/0000-0003-2965-1504
Minhaj AnsariDivision of Cardiology, Department of Medicine, University of California, SanFrancisco, Cardiology (San Francisco, CA, United States), 505 Parnassus Avenue, San Francisco, CA 94143, USA.ORCID https://orcid.org/0000-0002-9384-8881
Joshua P BarriosDivision of Cardiology, Department of Medicine, University of California, SanFrancisco, Cardiology (San Francisco, CA, United States), 505 Parnassus Avenue, San Francisco, CA 94143, USA.ORCID https://orcid.org/0000-0003-1618-6244
Geoffrey H TisonDivision of Cardiology, Department of Medicine, University of California, SanFrancisco, Cardiology (San Francisco, CA, United States), 505 Parnassus Avenue, San Francisco, CA 94143, USA.ORCID https://orcid.org/0000-0002-0310-3326
Jacques DelfrateDivision of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, Montreal, QC H1T 1C8, Canada.ORCID https://orcid.org/0009-0001-0466-1354
Robert AvramDivision of Cardiology, Department of Medicine, Montreal Heart Institute, 5000 Belanger Street, Montreal, QC H1T 1C8, Canada.ORCID https://orcid.org/0000-0002-8490-0270

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Right ventricular systolic function (RVSF) is a critical determinant of cardiovascular outcomes, yet assessment during coronary angiography remains challenging without prior imaging. We developed and validated DeepRV, a deep learning model predicting RVSF from routine coronary angiograms. Methods and results: DeepRV, a video-based deep neural network, was developed using 8053 coronary angiography studies from 6923 patients at Montreal Heart Institute (2017-23), with RVSF determined by echocardiography. The model was externally validated at the University of California, San Francisco, and prospectively deployed during primary percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI). In the internal test set ( Conclusion: DeepRV enables automated RVSF assessment from routine coronary angiograms and enhances diagnostic accuracy across experience levels. Real-time inference and open-weight availability support its potential as a point-of-care tool for risk stratification during coronary angiography.

Indexed as

AngiogramArtificial intelligenceAutomated interpretationCoronary angiographyMachine learningRight ventricleRight ventricular systolic functionTransthoracic echocardiogram

Identifiers

PMID42146868
PMCPMC13174274

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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.