Evidence map›Paper›PMID 42230922›Full record

ArticleScientific reports2026

A comparison of vendor artificial intelligence solutions for automated post-processing of short-axis cine images in cardiovascular magnetic resonance imaging.

Thomas Hadler, Clemens Ammann, Hadil Saad, Yashraj Bhoyroo, Jana Veit, Teodora Chitiboi, Jens Wetzl, Christian Geppert, Jeanette Schulz-Menger

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

9 authors.

Thomas HadlerCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany. thomas.hadler@charite.de.
Clemens AmmannCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Hadil SaadCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Yashraj BhoyrooWorking Group On CMR, Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Lindenberger Weg 80 13125, Berlin, Germany.
Jana VeitWorking Group On CMR, Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité - Universitätsmedizin Berlin, Lindenberger Weg 80 13125, Berlin, Germany.
Teodora ChitiboiDigital Technology and Innovation, Siemens Healthineers AG, Erlangen, Germany.
Jens WetzlResearch & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.
Christian GeppertResearch & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany.
Jeanette Schulz-MengerCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität Zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automated segmentation of cardiac magnetic resonance (CMR) imaging is integrated into clinical workflows, yet comparative performance across vendor AI solutions remains insufficiently characterized. This study assessed three models (two commercial, one research) for short-axis cine segmentation in a diverse cohort of 346 cases, including dilated cardiomyopathy (DCM), left ventricular hypertrophy (LVH), healthy volunteers, and other cardiac diseases. Clinical parameter agreement between AI-derived and expert-derived ventricular volumes and left ventricular mass (LVM) was evaluated using correlations and mean differences, segmentation agreement with Dice coefficient, and slice detection was characterized with false positive and negative rates (FPR/FNR). Papillary muscle (PM) inclusion was examined with subgroup analyses. AI-derived clinical parameters agreed strongly with expert measurements (r > 0.8). Nevertheless, inter-model biases included differing ventricular volume estimates. Midventricular segmentation was reliable (Dice > 80%), whereas apical slices were poor (Dice < 65%) with minor area impact (< 1cm

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedMagnetic Resonance Imaging, CineAdultAgedFemaleHeart VentriclesHumansHypertrophy, Left VentricularMaleMiddle AgedAI comparisonArtificial intelligenceAutomated post-processingCMR image analysisMachine learning

Identifiers

PMID42230922
PMCPMC13234427

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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.