Evidence map›Paper›PMID 40259202›Full record

ArticleJournal of imaging informatics in medicine2026

Diagnosing Severe Low-Gradient vs Moderate Aortic Stenosis with Artificial Intelligence Based on Echocardiography Images.

Michał Wrzosek, Mikolaj Buchwald, Patryk Czernik, Szymon Kupinski, Karina Zatorska, Anna Jasińska, Dariusz Zakrzewski, Juliusz Pukacki, Cezary Mazurek, Robert Pękal and 1 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07144189 (Artificial Intelligence-Based Assessment of Low-Gradient Aortic Stenosis Severity Using Echocardiographic Images), which is not on this 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.

NCT07144189 recruitingnot on this map

Artificial Intelligence-Based Assessment of Low-Gradient Aortic Stenosis Severity Using Echocardiographic Images

TypeobservationalSponsorNational Institute of Cardiology, Warsaw, PolandRan2025 to 2026Enrolled300ConditionsLow-gradient Aortic Stenosis, Aortic StenosisArmsAI diagnostic test for severe low-gradient aortic stenosis
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Clinical Applications of Artificial Intelligence in Structural Heart Disease.Journal of the Society for Cardiovascular Angiography & Interventions · 2026
    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

11 authors.

Michał WrzosekDepartment of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland.
Mikolaj BuchwaldPoznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland. mikolaj.buchwald@gmail.com.ORCID http://orcid.org/0000-0001-8764-0032
Patryk CzernikPoznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland.
Szymon KupinskiPoznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland.
Karina ZatorskaDepartment of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland.
Anna JasińskaDepartment of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland.
Dariusz ZakrzewskiDepartment of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland.
Juliusz PukackiPoznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland.
Cezary MazurekPoznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland.
Robert PękalPoznan Supercomputing and Networking Center, Polish Academy of Sciences, Poznan, Poland.
Tomasz HryniewieckiDepartment of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diagnosis of aortic valve stenosis (AS) is performed manually by a physician experienced in echocardiography imaging. A specific subtype of AS, a severe low-gradient AS, is the most challenging one in terms of differentiating it from the moderate AS. In this study, an artificial intelligence (AI)-based model was used to diagnose the severe low-gradient AS in a fully automatic manner. Data from 158 consecutive patients undergoing echocardiography examination to assess AS severity were used. The obtained performance of our fully automatic approach was AUC = 0.719, 95% confidence interval, 0.640-0.798. It is an important step towards a comprehensive and automatic, image-only-based clinical decision support system for determining the presence of AS and its severity, especially in AS subtypes, such as low-gradient AS.

Indexed as

Aortic Valve StenosisArtificial IntelligenceEchocardiographyImage Interpretation, Computer-AssistedAgedAged, 80 and overFemaleHumansMaleMiddle AgedSeverity of Illness IndexAortic stenosisArtificial intelligenceClinical decision support systemEchocardiographyLow-gradient aortic stenosis

Identifiers

PMID40259202
PMCPMC12921054

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Registered trials

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.