Evidence map›Paper›PMID 42881254›Full record

ArticleNature cardiovascular research2026

Deep learning-enabled assessment of right ventricular size and function is associated with cardiovascular outcomes.

Mostafa A Al-Alusi, Kavya Kopparapu, Pulkit Singh, Paolo di Achille, Christopher Reeder, Emily S Lau, Tal Shnitzer, Carl T Andrews, Shinwan Kany, Joel T Rämö and 8 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature cardiovascular research, 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

18 authors.

Mostafa A Al-AlusiDivision of Cardiovascular Medicine, Mass General Brigham Heart and Vascular Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0002-9359-1641
Kavya KopparapuCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Pulkit SinghCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Paolo di AchilleCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Christopher ReederCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-3893-2423
Emily S LauDivision of Cardiovascular Medicine, Mass General Brigham Heart and Vascular Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0001-9361-6397
Tal ShnitzerCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Carl T AndrewsDivision of Cardiovascular Medicine, Mass General Brigham Heart and Vascular Institute, Boston, MA, USA.
Shinwan KanyCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Joel T RämöCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-6429-5149
Julian S HaimovichDivision of Cardiovascular Medicine, Mass General Brigham Heart and Vascular Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0003-4346-6241
Shaan KhurshidDivision of Cardiovascular Medicine, Mass General Brigham Heart and Vascular Institute, Boston, MA, USA.
Michael H PicardDivision of Cardiovascular Medicine, Mass General Brigham Heart and Vascular Institute, Boston, MA, USA.
Jennifer E HoCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Puneet BatraCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Mahnaz MaddahCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-9837-6000
Steven A LubitzDivision of Cardiovascular Medicine, Mass General Brigham Heart and Vascular Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0002-9599-4866
Patrick T EllinorDivision of Cardiovascular Medicine, Mass General Brigham Heart and Vascular Institute, Boston, MA, USA. ellinor@mgh.harvard.edu.ORCID http://orcid.org/0000-0002-2067-0533

Funding

Using Electrocardiogram Genetics to Inform Arrhythmia RiskR01HL157635 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI ELLINOR, PATRICK THOMAS, MIRSHAHI, TOORAJ · 2022 to 2025
$2.9M
Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimationK23HL169839 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Shaan Khurshid · 2023 to 2026
$860k
Mentoring in Patient-Oriented and Translational HFpEF ResearchK24HL153669 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Jennifer E Ho · 2020 to 2026
$744k
American Heart Association (American Heart Association, Inc.) 18SFRN34230127American Heart Association (American Heart Association, Inc.) 18SFRN34250007American Heart Association (American Heart Association, Inc.) 23CDA1050571American Heart Association (American Heart Association, Inc.) 961045NHLBI NIH HHS K23 HL169839U.S. Department of Health & Human Services | National Institutes of Health (NIH) 1R01HL139731U.S. Department of Health & Human Services | National Institutes of Health (NIH) K23-HL159243U.S. Department of Health & Human Services | National Institutes of Health (NIH) K23HL16989U.S. Department of Health & Human Services | National Institutes of Health (NIH) K24 HL153669U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01HL157635U.S. Department of Health & Human Services | National Institutes of Health (NIH) RO1HL157635U.S. Department of Health & Human Services | National Institutes of Health (NIH) T32-HL007208
6 · The paper itself

Abstract

Right ventricular (RV) size and function inform prognosis in many cardiovascular diseases, but assessment by two-dimensional transthoracic echocardiography is challenging. Here we show that a deep learning model, Dimensional Reconstruction of Imaging Data-Right Ventricle (DROID-RV), can estimate RV end-diastolic diameter and identify RV systolic dysfunction from transthoracic echocardiography videos. We trained DROID-RV in 17,141 ambulatory cardiology patients (51,188 studies and 383,881 video clips) and validated model predictions in 9,112 primary care patients and 831 patients with RV ejection fraction assessed by three-dimensional transthoracic echocardiography. Across multiple test samples, DROID-RV accurately estimates RV end-diastolic diameter and identifies RV dysfunction, although model prediction error increases at extremes of RV size and function. We additionally show that RV size and function predictions are associated with incident mortality, atrial fibrillation and heart failure. Our work demonstrates how deep learning can automate RV assessment and risk stratification at scale using two-dimensional echocardiography.

Identifiers

PMID42881254

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.