Evidence map›Paper›PMID 41660663›Full record

ArticleEuropean heart journal. Digital health2026

Artificial-intelligence-enabled digital stethoscope improves point-of-care screening for moderate-to-severe valvular heart disease.

Moshe Rancier, Igor Israel, Vimalson Monickam, Caroline Currie, Ben Verschoore, Emileigh Lastowski, Douglas W Van Pelt, John Prince, Rosalie V McDonough

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. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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

9 authors.

Moshe RancierAdult Primary Care, Mass General Brigham Community Physicians, Lawrence, MA, USA.
Igor IsraelMedicine, Parker Jewish Institute for Health Care and Rehabilitation, New Hyde Park, NY, USA.
Vimalson MonickamMedicine, Parker Jewish Institute for Health Care and Rehabilitation, New Hyde Park, NY, USA.
Caroline CurrieEko Health, Inc. 2100 Powell Street, Ste. 300, Emeryville, CA 94608, USA.
Ben VerschooreEko Health, Inc. 2100 Powell Street, Ste. 300, Emeryville, CA 94608, USA.
Emileigh LastowskiEko Health, Inc. 2100 Powell Street, Ste. 300, Emeryville, CA 94608, USA.
Douglas W Van PeltEko Health, Inc. 2100 Powell Street, Ste. 300, Emeryville, CA 94608, USA.
John PrinceEko Health, Inc. 2100 Powell Street, Ste. 300, Emeryville, CA 94608, USA.
Rosalie V McDonoughEko Health, Inc. 2100 Powell Street, Ste. 300, Emeryville, CA 94608, USA.ORCID https://orcid.org/0000-0002-8060-4490

Funding

Deep Learning for Automated Aortic Stenosis and Valvular Heart Disease Detection Using a Digital StethoscopeR44HL144297 · NHLBI · EKO DEVICES, INC. · PI THOMAS, JAMES DAVID, VENKATRAMAN, SUBRAMANIAM · 2020 to 2022
$2.8M
NHLBI NIH HHS R44 HL144297
6 · The paper itself

Abstract

Aims: Valvular heart disease (VHD) can affect more than one in two adults over 65 yet remains underdiagnosed due to the limitations of traditional auscultation. Earlier detection is critical for improving outcomes, but many cases go unrecognized. This study evaluates whether an artificial intelligence (AI)-enabled digital stethoscope can augment primary care providers' (PCPs) ability to detect clinically significant VHD compared with analogue auscultation alone. Methods and results: In this prospective study, 357 patients aged ≥50 years at risk for heart disease underwent both analogue cardiac auscultation by PCPs (standard of care, SOC) and digital cardiac auscultation by study coordinators followed by AI analysis using an electronic stethoscope (AI-augmented). Echocardiography and audible murmur annotation served as the reference standard. Sensitivity and specificity of AI vs. SOC were compared using Fisher's exact test. The AI-augmented system demonstrated significantly higher sensitivity (92.3% vs. 46.2%, Conclusion: Artificial intelligence-enabled digital stethoscopes significantly improve point-of-care VHD detection, offering a promising tool for earlier diagnosis and intervention in primary care settings.

Indexed as

Artificial intelligenceDigital stethoscopeEarly detectionPrimary careStructural murmurValvular heart disease

Identifiers

PMID41660663
PMCPMC12873438

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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.