Evidence map›Paper›PMID 42516546›Full record

ArticleEuropean heart journal. Imaging methods and practice2026

Fully automated deep learning MAPSE: retrospective analysis and real-time clinical application.

Maria Muan Haga, Nora Lindeman Katla, Vegard Holmstrøm, Espen Holte, Stian Stølen, Knut Haakon Stensæth, Andreas Østvik, Lasse Løvstakken, Håvard Dalen, Erik Smistad and 1 more

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Article in European heart journal. Imaging methods and practice, 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

11 authors.

Maria Muan HagaDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.
Nora Lindeman KatlaDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.
Vegard HolmstrømDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.
Espen HolteDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.ORCID https://orcid.org/0000-0002-8891-1372
Stian StølenDepartment of Cardiology and Thoracic Surgery, St.Olavs University Hospital, Trondheim, Norway.
Knut Haakon StensæthDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.
Andreas ØstvikDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.ORCID https://orcid.org/0000-0003-3895-2683
Lasse LøvstakkenDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.ORCID https://orcid.org/0000-0003-2474-9588
Håvard DalenDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.ORCID https://orcid.org/0009-0003-4000-6298
Erik SmistadDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.ORCID https://orcid.org/0000-0002-7258-4709
Bjørnar GrenneDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Box 8905 Trondheim 7491, Norway.ORCID https://orcid.org/0000-0002-2984-6865

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Mitral annular plane systolic excursion (MAPSE) is an accessible echocardiographic measure of left ventricular (LV) function. However, manual measurement methods are operator-dependent and time-consuming. We developed a multistep deep learning (DL) method for off-line and real-time fully automated MAPSE estimation, and aimed to assess agreement, reproducibility, time efficiency, and feasibility compared with standard manual measurements. Methods and results: The DL-based method was evaluated in two retrospective cohorts ( Conclusion: This novel DL method for fully automated MAPSE demonstrated excellent feasibility, robust reproducibility, and good agreement with both manual M-mode and CMR-derived measurements. Automated DL-MAPSE could substantially reduce analysis time and enhance reproducibility, increasing its clinical value as a marker of LV systolic function.

Indexed as

artificial intelligencedeep learningechocardiographyMAPSEreal time

Identifiers

PMID42516546
PMCPMC13403282

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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.