Evidence map›Paper›PMID 37611002›Full record

ArticleEuropean heart journal2023

Severe aortic stenosis detection by deep learning applied to echocardiography.

Gregory Holste, Evangelos K Oikonomou, Bobak J Mortazavi, Andreas Coppi, Kamil F Faridi, Edward J Miller, John K Forrest, Robert L McNamara, Lucila Ohno-Machado, Neal Yuan and 5 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 4 pooled it
25.7field-weighted citation impact, top 1% of its field
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

71 citing papers in PubMed, 4 syntheses or guidelines pooled it, 117 citations in OpenAlex.

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  17. Automated Phenotyping of Mitral Stenosis Using Deep Learning.medRxiv : the preprint server for health sciences · 2026
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11 more citing papers are in PubMed but not listed here.

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 at 5 institutions in 1 country.

Gregory HolsteDepartment of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.ORCID 0000-0002-5657-3081
Evangelos K OikonomouSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA.ORCID 0000-0003-4362-0720
Bobak J MortazaviDepartment of Computer Science & Engineering, Texas A&M University, College Station, TX, USA.
Andreas CoppiSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA.
Kamil F FaridiSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA.
Edward J MillerSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA.ORCID 0000-0002-2156-5962
John K ForrestSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA.
Robert L McNamaraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA.
Lucila Ohno-MachadoSection of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.ORCID 0000-0002-8005-7327
Neal YuanDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA.
Aakriti GuptaDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
David OuyangDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Harlan M KrumholzSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA.
Zhangyang WangDepartment of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
Rohan KheraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA.ORCID 0000-0001-9467-6199
Yale University · USYale New Haven Hospital · USCedars-Sinai Medical Center · USSan Francisco VA Medical Center · USThe University of Texas at Austin · US

Funding

Evaluating and Improving Utilization of Evidence-Based Medical Therapy in Patients with Heart Failure using Automated Tools in the Electronic Health RecordK23HL153775 · NHLBI · YALE UNIVERSITY · PI KHERA, ROHAN · 2021 to 2025
$918k
A multi-modal approach for efficient, point-of-care screening of hypertrophic cardiomyopathyF32HL170592 · NHLBI · YALE UNIVERSITY · PI OIKONOMOU, EVANGELOS · 2023 to 2024
$166k
NHLBI NIH HHS F32 HL170592NHLBI NIH HHS K23 HL153775
6 · The paper itself

Abstract

BACKGROUND AND

aimsEarly diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. METHODS AND

resultsIn a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity.

conclusionThis study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening.

Indexed as

Aortic Valve StenosisDeep LearningAortic ValveEchocardiographyHumansUltrasonographyAortic stenosisDeep learningDigital healthEchocardiography

Identifiers

PMID37611002
PMCPMC11004929
OpenAlexW4386090740

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.