{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T05:07:33Z","timestamp":1783832853409,"version":"3.55.0"},"reference-count":53,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T00:00:00Z","timestamp":1776556800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T00:00:00Z","timestamp":1776556800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1 TR001872"],"award-info":[{"award-number":["UL1 TR001872"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["5T32HL007185\u201047"],"award-info":[{"award-number":["5T32HL007185\u201047"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Poor cardiac MR image quality can prompt repeat examinations and hinder clinical decision\u2010making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate whether pre\u2010imaging clinical information, extracted using a large language model (LLM), is independently associated with cardiac MR image quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>1006 adults undergoing clinical cardiac MR examinations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>1.5\u2009T and 3\u2009T scanners with cine, black blood, MR angiogram, or late gadolinium enhancement protocols.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>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\u2010compliant 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\u2010imaging\u2010adjusted image quality label was used in a sensitivity analysis. Pre\u2010imaging clinical and patient variables were extracted from electronic health records. Associations between variables and image quality labels were investigated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Cohen's kappa (\u03ba) for label agreement. Chi\u2010square and t\u2010tests for univariate analysis. Variance inflation factor (VIF) and multivariable logistic regression. Significance level:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Binarized image quality labels showed substantial agreement with interpreters' assessments for both the primary dataset (\u03ba\u2009=\u20090.689) and the repeat\u2010adjusted dataset (\u03ba\u2009=\u20090.879). There was no significant multicollinearity (VIF\u2009=\u20091.01\u20131.39). Cognitive and communication impairment (OR 1.81, 95% CI [1.30\u20132.54],\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and respiratory issues (1.57 [1.14\u20132.17],\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.006) were independently associated with poor image quality. These associations remained significant in the repeat\u2010adjusted sensitivity analysis (cognitive and communication impairment (OR 1.75, 95% CI [1.27\u20132.44],\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and respiratory compromise (OR 1.37, 95% CI [1.04\u20131.82],\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.027)). Other clinical variables were not independently associated after adjustment.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>Cognitive\/communication impairment and respiratory compromise were independently associated with poor cardiac MR image quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>3.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>Stage 2.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.70336","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T05:50:59Z","timestamp":1776664259000},"page":"565-578","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pre\u2010Imaging Clinical Factors Associated With Cardiac\n                    <scp>MR<\/scp>\n                    Image Quality Using Large Language Model\u2010Enabled Data Extraction"],"prefix":"10.1002","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3725-4763","authenticated-orcid":false,"given":"Hong","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Radiology Affiliated Hospital of Zunyi Medical University  Zunyi Guizhou China"},{"name":"Department of Radiology and Biomedical Imaging University of California  San Francisco California 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