Evidence map›Paper›PMID 42003050›Full record

ArticleJournal of magnetic resonance imaging : JMRI2026

Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.

Hong Yu, Masha Bondarenko, Ali Nowroozi, Yoo Jin Lee, Adrian Serapio, Punita Kaveti, Jae Ho Sohn

Abstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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. Article
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

7 authors.

Hong YuDepartment of Radiology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.ORCID https://orcid.org/0000-0002-3725-4763
Masha BondarenkoDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID https://orcid.org/0009-0006-3471-7095
Ali NowrooziDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID https://orcid.org/0000-0001-7250-891X
Yoo Jin LeeDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID https://orcid.org/0009-0008-2348-2520
Adrian SerapioDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID https://orcid.org/0009-0005-0171-2151
Punita KavetiDivision of Cardiology and Department of Medicine, University of California, San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0001-5461-1518
Jae Ho SohnDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-6733-7551

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
MUTIDISCIPLINARY TRAINING PROGRAM IN LUNG DISEASEST32HL007185 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI David J Erle, LAURENCE HUANG · 1985 to 2026
$24.6M
National Heart, Lung, and Blood Institute (NHLBI), NIH 5T32HL007185-47NCATS NIH HHS UL1 TR001872NHLBI NIH HHS T32 HL007185
6 · The paper itself

Abstract

backgroundPoor cardiac MR image quality can prompt repeat examinations and hinder clinical decision-making. PURPOSE: To evaluate whether pre-imaging clinical information, extracted using a large language model (LLM), is independently associated with cardiac MR image quality. STUDY TYPE: Retrospective. POPULATION: 1006 adults undergoing clinical cardiac MR examinations. FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T scanners with cine, black blood, MR angiogram, or late gadolinium enhancement protocols. ASSESSMENT: Image quality was categorized per study as excellent, slightly limited, severely limited, or nondiagnostic using institutional reporting conventions finalized by radiologists and cardiologists. A HIPAA-compliant LLM assigned image quality labels based on radiology reports through an iteratively refined prompt, with reliability confirmed by two radiologists. Labels were binarized as Good (excellent and slightly limited) versus Poor (severely limited and nondiagnostic). A repeat-imaging-adjusted image quality label was used in a sensitivity analysis. Pre-imaging clinical and patient variables were extracted from electronic health records. Associations between variables and image quality labels were investigated. STATISTICAL TESTS: Cohen's kappa (κ) for label agreement. Chi-square and t-tests for univariate analysis. Variance inflation factor (VIF) and multivariable logistic regression. Significance level: p < 0.05.

resultsBinarized image quality labels showed substantial agreement with interpreters' assessments for both the primary dataset (κ = 0.689) and the repeat-adjusted dataset (κ = 0.879). There was no significant multicollinearity (VIF = 1.01-1.39). Cognitive and communication impairment (OR 1.81, 95% CI [1.30-2.54], p < 0.001) and respiratory issues (1.57 [1.14-2.17], p = 0.006) were independently associated with poor image quality. These associations remained significant in the repeat-adjusted sensitivity analysis (cognitive and communication impairment (OR 1.75, 95% CI [1.27-2.44], p < 0.001) and respiratory compromise (OR 1.37, 95% CI [1.04-1.82], p = 0.027)). Other clinical variables were not independently associated after adjustment. DATA

conclusionCognitive/communication impairment and respiratory compromise were independently associated with poor cardiac MR image quality. LEVEL OF EVIDENCE: 3: TECHNICAL EFFICACY: Stage 2.

Indexed as

HeartImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMagnetic Resonance ImagingAdultAgedFemaleGadoliniumHumansLarge Language ModelsMagnetic Resonance Imaging, CineMaleMiddle AgedReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityGadoliniumartificial intelligencecardiac MRimage qualitylarge language model

Identifiers

PMID42003050
PMCPMC13356445

What OpenQuestion holds

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Registered trials

None linked

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.