ArticleCirculation. Cardiovascular imaging2024
Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.
Erratum issuedAbstract readMulticenter Study
In one paragraph
Article in Circulation. Cardiovascular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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18citing papers in PubMed
–field-weighted citation impact
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Who cites it
18 citing papers in PubMed.
- Advanced quantitative CT for coronary artery disease: integrating stenosis quantification, plaque quantification, and fractional flow reserve-CT.The British journal of radiology · 2026Review
- High scan-rescan repeatability of AI-enabled coronary plaque quantification from coronary CT angiography.European radiology · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging.Korean journal of radiology · 2026Review
- Prognostic value of AI-enabled quantitative coronary CT angiography for major adverse cardiovascular events: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Artificial intelligence-enabled plaque characterization from coronary computed tomography establishes basis of angina in women with nonobstructive atherosclerosis.Communications medicine · 2026Article
- Deep Learning-based Automated Coronary Plaque Quantification: First Demonstration With Ultra-high Resolution Photon-counting Detector CT at Different Temporal Resolutions.Investigative radiology · 2026Article
- A narrative review on the use of artificial intelligence in cardiovascular medicine.Cardiovascular diagnosis and therapy · 2026Review
- Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group.Nature reviews. Cardiology · 2026Review
- The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.Frontiers in immunology · 2026Review
- Cardiac CT for personalized phenotyping in stable coronary artery disease: toward precision medicine.BJR open · 2026Review
- Acute Myocardial Infarction: Molecular Pathogenesis, Diagnosis, and Clinical Management.MedComm · 2025Review
- Review
- Application of non-invasive imaging in myocardial infarction: a bibliometric analysis from January 2003 to December 2022.Quantitative imaging in medicine and surgery · 2025Article
- Coronary plaque characteristics quantified by artificial intelligence-enabled plaque analysis: Insights from a multi-ethnic asymptomatic US population.American journal of preventive cardiology · 2025Article
- Can AI Capture and Quantify Clinical Expertise? Implications for Intracoronary Imaging in Percutaneous Coronary Intervention.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- The unmet promise: a critical review of antioxidant strategies in myocardial ischemia-reperfusion injury and the path towards precision medicine.Frontiers in pharmacology · 2025Review
- Can Artificial Intelligence Help Us in the Evaluation of Coronary Artery Calcification Scores by Acting as a Prognosticator in Patients That Are Operated on Due to Non-Small Cell Lung Cancer? A Pivotal Study.Journal of clinical medicine · 2024Article
- Isolated Perfused Hearts for Cardiovascular Research: An Old Dog with New Tricks.Journal of cardiovascular translational research · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
33 authors.
Robert J H MillerBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0003-4676-2433
Nipun ManralBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0001-9040-6896
Andrew LinBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).
Aakash ShanbhagBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0001-5199-3071
Caroline ParkBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0001-9930-073X
Jacek KwiecinskiDepartment of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland (J.K.).ORCID 0000-0001-8202-6359
Aditya KillekarBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0003-3831-5676
Priscilla McElhinneyBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).
Hidenari MatsumotoDivision of Cardiology, Showa University School of Medicine, Tokyo, Japan (H.M.).
Aryabod RazipourBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).
Kajetan GrodeckiBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0002-0358-5679
Alan C KwanBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0002-4393-1011
Donghee HanBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0003-1491-3612
Keiichiro KuronumaBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).
Guadalupe Flores TomasinoBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0002-5615-7280
Jolien GeersBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).
Markus GoellerDepartment of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany (M.G., M.M., S.A.).
Mohamed MarwanDepartment of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany (M.G., M.M., S.A.).ORCID 0000-0001-8759-4101
Heidi GransarBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0003-4704-8290
Balaji K TamarappooDepartment of Cardiology, Banner University Medical Center Phoenix, AZ (B.K.T.).
Sebastien CadetBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0002-9567-6438
Victor Y ChengDepartment of Cardiology, Oklahoma Heart Institute, Tulsa, OK (V.Y.C.).
Stephan AchenbachDepartment of Cardiology, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany (M.G., M.M., S.A.).ORCID 0000-0002-7596-095X
Stephen J NichollsVictorian Heart Institute, Monash University, Melbourne, Victoria, Australia (A.L., S.J.N., D.T.W.).
Dennis T WongVictorian Heart Institute, Monash University, Melbourne, Victoria, Australia (A.L., S.J.N., D.T.W.).
Lu ChenDivision of Cardiac Imaging, St. Francis Hospital and Heart Center, Roslyn, NY (L.C., J.J.C.).ORCID 0000-0002-9849-7339
J Jane CaoDivision of Cardiac Imaging, St. Francis Hospital and Heart Center, Roslyn, NY (L.C., J.J.C.).ORCID 0000-0002-8890-0542
Daniel S BermanBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0002-3793-9578
Marc R DweckBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, United Kingdom (M.R.D., D.E.N., M.C.W.).ORCID 0000-0001-9847-5917
David E NewbyBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, United Kingdom (M.R.D., D.E.N., M.C.W.).ORCID 0000-0001-7971-4628
Michelle C WilliamsBritish Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, United Kingdom (M.R.D., D.E.N., M.C.W.).ORCID 0000-0003-3556-2428
Piotr J SlomkaBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0002-6110-938X
Damini DeyBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA (R.J.J.M., N.M., A.L., A.S., C.P., A.K., P.M., A.R., K.G., A.C.K., D.H., K.K., G.F.T., J.G., H.G., S.C., D.S.B., P.J.S., D.D.).ORCID 0000-0003-2236-6970
Funding
Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT AngiographyR01HL148787 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2020 to 2023
$3.0MAI-enabled characterization of fibrocalcific aortic valve disease and inflammation from CT Angiography: Patient-specific outcome predictionR01HL175875 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Damini Dey · 2024 to 2026
$2.4MEffect of Intensive Medical Treatment on Quantified Coronary Artery Plaque Components with Serial Coronary CTA in Women with Non-Obstructive CADR01HL151266 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI, TAMARAPPOO, BALAJI K. · 2020 to 2023
$1.6MBritish Heart Foundation CH/09/002/26360NHLBI NIH HHS R01 HL148787NHLBI NIH HHS R01 HL151266NHLBI NIH HHS R01 HL175875Wellcome Trust
Abstract
backgroundPlaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples.
methodsIn this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction.
resultsQuantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47-4.78];
conclusionsPer-patient age- and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.
Indexed as
Computed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseDeep LearningMyocardial InfarctionPlaque, AtheroscleroticAgedAge FactorsCoronary VesselsFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisRisk Assessmentcardiac imaging techniquescoronary artery diseasedeep learningmyocardial infarctionplaque, atherosclerotic
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