Evidence map›Paper›PMID 41327147›Full record

ArticleBMC medical informatics and decision making2025

A semi-automated quality assurance tool for cardiovascular magnetic resonance imaging: application to outlier detection, artificial intelligence evaluation and trainee feedback.

Thomas Hadler, Leonhard Grassow, Johanna Kuhnt, Richard Hickstein, Hadil Saad, Maximilian Fenski, Jan Gröschel, Ralf-Felix Trauzeddel, Edyta Blaszczyk, Clemens Ammann and 4 more

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Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

14 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.
Leonhard GrassowCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Johanna KuhntCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Richard HicksteinCharité - 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.
Maximilian FenskiCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Jan GröschelCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Ralf-Felix TrauzeddelCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Edyta BlaszczykCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Clemens AmmannCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Darian ViezzerCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Anja HennemuthCharité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ECRC Experimental and Clinical Research Center, Berlin, Germany.
Steffen LangeDepartment of Computer Sciences, Hochschule Darmstadt - University of Applied Sciences, Darmstadt, 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

backgroundCardiovascular magnetic resonance (CMR) offers state-of-the-art volume, function, fibrosis and oedema imaging. Quality assurance (QA) tasks, such as quantitative parameter reproducibility assessments, the evaluation of AI methods, and the assessment of trainees have become essential to CMR. However, the explainability of how qualitative differences impact quantitative differences remains underexplored. Our aim is to demonstrate a semi-automated QA tool, Lazy Luna's (LL) applicability to typical CMR QA application cases.

methodsA software feature error-tracing is designed that allows for quickly pinpointing qualitative reasons for quantitative differences and outliers. Three QA application cases were designed. First, LL was applied to perform outlier detection for inter- and intraobserver analyses to detect failure cases and provide qualitative explanations. Outlier detection was performed on several typical images types. Second, LL supported an Artificial intelligence (AI) evaluation, in which an AI method was compared to a CMR-expert of 144 patients. LL assessed the acceptability of AI biases for left and right ventricular (LV, RV) end-systolic, -diastolic, and stroke volumes (ESV, EDV, SV), ejection fractions (EF) and the myocardial mass (LVM). Annotations were examined to explain the qualitative differences that resulted in good and poor parameters. The AI investigation was recorded as a video. Third, LL was used to provide a Trainee Feedback to a CMR beginner. The trainee was compared to an expert on several imaging techniques to investigate outliers.

resultsFor the outlier detection, LL detected segmentation differences that caused parameter differences on multiple sequences. For the AI evaluation calculated clinical parameter biases to be: LVESV:-3.1 ml, LVEDV:2.1 ml, LVSV:6.5 ml, LVEF:3.0 ml, RVESV:0.3 ml, RVEDV:-3.8 ml, RVSV:-4.2 ml, RVEF:-1.4 ml, LVM:-2 g. Inspecting the causes for outlier differences revealed that juxtaposed basal slice failures caused unacceptable LVSV deviations between AI and expert. For the trainee assessment, LL showed that trainee parameters exceeded tolerance ranges. The segmentations could be improved to better mirror expert segmentations and close the parameter gaps.

conclusionLazy Luna, as a semi-automated quality assurance tool, is applicable to several quality assurance application cases in CMR.

Indexed as

Artificial IntelligenceMagnetic Resonance ImagingQuality Assurance, Health CareHumansArtificial intelligenceCardiovascular magnetic resonanceEducationQuality assuranceQuantitative parametersSoftwareStatistical analysis

Identifiers

PMID41327147
PMCPMC12670863

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LicenceCC BY
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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.