AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review

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dc.article.number345
dc.catalogadorgjm
dc.contributor.authorJiménez Jara, Cristina
dc.contributor.authorSalas, Rodrigo
dc.contributor.authorDíaz Navarro, Rienzi
dc.contributor.authorChabert, Steren
dc.contributor.authorAndía Kohnenkampf, Marcelo Edgardo
dc.contributor.authorVega, Julián
dc.contributor.authorUrbina, Jesús
dc.contributor.authorUribe, Sergio
dc.contributor.authorSekine, Tetsuro
dc.contributor.authorRaimondi, Francesca
dc.contributor.authorSotelo, Julio
dc.date.accessioned2025-09-25T15:48:40Z
dc.date.available2025-09-25T15:48:40Z
dc.date.issued2025
dc.description.abstractCardiac magnetic resonance (CMR) imaging has become a key tool in evaluating myocardial injury secondary to coronary artery disease (CAD), providing detailed assessments of cardiac morphology, function, and tissue composition. The integration of artificial intelligence (AI), including machine learning and deep learning techniques, has enhanced the diagnostic capabilities of CMR by automating segmentation, improving image interpretation, and accelerating clinical workflows. Radiomics, through the extraction of quantitative imaging features, complements AI by revealing sub-visual patterns relevant to disease characterization. This systematic review analyzed AI applications in CMR for CAD. A structured search was conducted in MEDLINE, Web of Science, and Scopus up to 17 March 2025, following PRISMA guidelines and quality-assessed with the CLAIM checklist. A total of 106 studies were included: 46 on classification, 19 using radiomics, and 41 on segmentation. AI models were used to classify CAD vs. controls, predict major adverse cardiovascular events (MACE), arrhythmias, and post-infarction remodeling. Radiomics enabled differentiation of acute vs. chronic infarction and prediction of microvascular obstruction, sometimes from non-contrast CMR. Segmentation achieved high performance for myocardium (DSC up to 0.95), but scar and edema delineation were more challenging. Reported performance was moderate-to-high across tasks (classification AUC = 0.66–1.00; segmentation DSC = 0.43–0.97; radiomics AUC = 0.57–0.99). Despite promising results, limitations included small or overlapping datasets. In conclusion, AI and radiomics offer substantial potential to support diagnosis and prognosis of CAD through advanced CMR image analysis.
dc.fechaingreso.objetodigital2025-09-25
dc.format.extent30 páginas
dc.fuente.origenORCID
dc.identifier.doi10.3390/jcdd12090345
dc.identifier.urihttps://doi.org/10.3390/jcdd12090345
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/105760
dc.information.autorucEscuela de Medicina; Andía Kohnenkampf, Marcelo Edgardo; 0000-0002-1251-5832; 90691
dc.issue.numero9
dc.language.isoen
dc.nota.accesocontenido completo
dc.revistaJournal of Cardiovascular Development and Disease
dc.rightsacceso abierto
dc.rights.licenseCC BY 4.0 Attribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.subjectAI
dc.subjectSystematic review
dc.subjectArtificial intelligence
dc.subjectCAD
dc.subjectCoronary artery disease
dc.subjectCMR
dc.subjectCardiac magnetic resonance
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleAI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review
dc.typeartículo
dc.volumen12
sipa.codpersvinculados90691
sipa.trazabilidadORCID;2025-09-22
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