Evidence map›Paper›PMID 41489752›Full record

ReviewHeart failure reviews2026

Evaluation of the DAMSUN-HF trial: the role of an artificial intelligence stethoscope in detecting reduced ejection fraction in patients living in a low-resource region.

Dmitry Abramov, Baljash S Cheema, Kalliopi Keramida, Kaveh Hosseini, Marat Fudim, Abdul Mannan Khan Minhas

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Heart failure reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Dmitry AbramovDivision of Cardiology, Loma Linda University Health, Loma Linda, CA, USA. Dabramov@llu.edu.
Baljash S CheemaCenter for Artificial Intelligence, Northwestern Medicine, Bluhm Cardiovascular Institute, Chicago, IL, USA.
Kalliopi KeramidaCardiology Department, General Anti-Cancer Oncological Hospital Agios Savvas, Athens, Greece.
Kaveh HosseiniDepartment of Cardiology, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark.
Marat FudimDepartment of Medicine, Duke University Medical Center, Durham, NC, USA.
Abdul Mannan Khan MinhasSection of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluation of ejection fraction (EF) is paramount for patients with symptoms of heart failure. While transthoracic echocardiography (TTE) is the most common way to evaluate EF, recent advances in artificial intelligence (AI) have opened the door for alternative methods to screen for reduced EF with smaller and more portable technology. The DAMSUN-HF study evaluated the accuracy of an AI-based stethoscope for detecting reduced EF (≤40%) in patients with symptoms of heart failure in a region with geographic and economic barriers to obtaining timely TTE. This mini-review examines the DAMSUN-HF study and highlights the potential clinical implications of the study findings.

Indexed as

Artificial IntelligenceHeart FailureStethoscopesStroke VolumeEchocardiographyHumansArtificial intelligenceEchocardiographyEjection fractionHeart failure

Identifiers

PMID41489752
PMCPMC12769522

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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.