Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep le...

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Vydané v:Nature communications Ročník 12; číslo 1; s. 715 - 9
Hlavní autori: Zeleznik, Roman, Foldyna, Borek, Eslami, Parastou, Weiss, Jakob, Alexander, Ivanov, Taron, Jana, Parmar, Chintan, Alvi, Raza M., Banerji, Dahlia, Uno, Mio, Kikuchi, Yasuka, Karady, Julia, Zhang, Lili, Scholtz, Jan-Erik, Mayrhofer, Thomas, Lyass, Asya, Mahoney, Taylor F., Massaro, Joseph M., Vasan, Ramachandran S., Douglas, Pamela S., Hoffmann, Udo, Lu, Michael T., Aerts, Hugo J. W. L.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 29.01.2021
Nature Publishing Group
Nature Portfolio
Predmet:
631/114/1305
639/705/117
692/308/409
692/4019/2776
692/499
Aged
Arteriosclerosis
Artificial neural networks
Asymptomatic Diseases
Automation
Biomarkers
Calcification (ectopic)
Calcium
Calcium - analysis
Cardiovascular diseases
Cardiovascular Diseases - complications
Cardiovascular Diseases - diagnosis
Cardiovascular Diseases - epidemiology
Cardiovascular Diseases - pathology
Chest
Chest Pain - diagnosis
Chest Pain - etiology
Computed tomography
Coronary artery
Coronary vessels
Coronary Vessels - diagnostic imaging
Coronary Vessels - pathology
Deep Learning
Female
Follow-Up Studies
Health risks
Heart Disease Risk Factors
Humanities and Social Sciences
Humans
Image Processing, Computer-Assisted - methods
Male
Middle Aged
multidisciplinary
Neural networks
Reproducibility of Results
Retrospective Studies
Risk analysis
Risk Assessment - methods
Risk factors
Robustness
Science
Science (multidisciplinary)
Tomography
Tomography, X-Ray Computed
ISSN:2041-1723, 2041-1723
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Abstract Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health. Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.
AbstractList Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health. Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.
Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and time-efficient deep learning system to automatically quantify coronary calcium on CT scans and predict cardiovascular events in a large, multicentre study.
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.
ArticleNumber 715
Author Aerts, Hugo J. W. L.
Taron, Jana
Vasan, Ramachandran S.
Mahoney, Taylor F.
Kikuchi, Yasuka
Douglas, Pamela S.
Hoffmann, Udo
Alvi, Raza M.
Scholtz, Jan-Erik
Zeleznik, Roman
Zhang, Lili
Massaro, Joseph M.
Weiss, Jakob
Alexander, Ivanov
Banerji, Dahlia
Lu, Michael T.
Karady, Julia
Parmar, Chintan
Lyass, Asya
Foldyna, Borek
Uno, Mio
Mayrhofer, Thomas
Eslami, Parastou
Author_xml – sequence: 1
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  orcidid: 0000-0002-2433-9151
  surname: Zeleznik
  fullname: Zeleznik, Roman
  organization: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School
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  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
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  surname: Weiss
  fullname: Weiss, Jakob
  organization: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tübingen
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  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
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  givenname: Jana
  surname: Taron
  fullname: Taron, Jana
  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tübingen
– sequence: 7
  givenname: Chintan
  orcidid: 0000-0002-2140-814X
  surname: Parmar
  fullname: Parmar, Chintan
  organization: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School
– sequence: 8
  givenname: Raza M.
  surname: Alvi
  fullname: Alvi, Raza M.
  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
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  surname: Banerji
  fullname: Banerji, Dahlia
  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
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  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
– sequence: 11
  givenname: Yasuka
  surname: Kikuchi
  fullname: Kikuchi, Yasuka
  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University
– sequence: 12
  givenname: Julia
  surname: Karady
  fullname: Karady, Julia
  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University
– sequence: 13
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  surname: Zhang
  fullname: Zhang, Lili
  organization: Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School
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  orcidid: 0000-0001-6287-5185
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  surname: Lyass
  fullname: Lyass, Asya
  organization: Department of Mathematics and Statistics, Boston University
– sequence: 17
  givenname: Taylor F.
  orcidid: 0000-0002-5930-5672
  surname: Mahoney
  fullname: Mahoney, Taylor F.
  organization: Department of Biostatistics, Boston University School of Public Health
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  givenname: Joseph M.
  surname: Massaro
  fullname: Massaro, Joseph M.
  organization: Department of Biostatistics, Boston University School of Public Health
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  givenname: Ramachandran S.
  orcidid: 0000-0001-7357-5970
  surname: Vasan
  fullname: Vasan, Ramachandran S.
  organization: National Heart, Lung, and Blood Institute and Boston University, Framingham Heart Study, Departments of Cardiology and Preventive Medicine, Department of Medicine, Boston University School of Medicine
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  orcidid: 0000-0002-2122-2003
  surname: Aerts
  fullname: Aerts, Hugo J. W. L.
  email: haerts@bwh.harvard.edu
  organization: Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Department of Radiology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33514711$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1016/j.media.2016.04.004
10.21037/tlcr.2018.05.05
10.1007/s00330-012-2726-5
10.1016/j.jcmg.2017.01.030
10.1016/j.jcmg.2018.10.026
10.1161/CIRCULATIONAHA.107.699579
10.1016/j.ahj.2012.01.028
10.1007/s12350-018-1292-x
10.1109/TMI.2017.2769839
10.1016/j.jacc.2018.11.002
10.1148/radiol.2020191621
10.1161/CIRCGENETICS.111.961342
10.1093/aje/kwm021
10.1177/001316446002000104
10.1016/j.jcct.2014.06.002
10.1016/j.amjcard.2008.06.038
10.1111/j.1746-1561.2007.00249.x
10.1161/CIRCULATIONAHA.115.018524
10.1093/ije/dyv337
10.1001/jamacardio.2016.5922
10.1093/oxfordjournals.aje.a112819
10.1117/12.2512541
10.1016/j.ahj.2014.03.003
10.1161/CIRCIMAGING.117.006776
10.1007/978-981-10-5122-7_94
10.1038/s41568-018-0016-5
10.1093/eurheartj/ehy217
10.1016/j.jcmg.2017.09.003
10.1007/s12410-016-9372-2
10.1093/oxfordjournals.aje.a112813
10.1056/NEJMoa1102873
10.1109/TMI.2019.2899534
10.1038/s41551-018-0195-0
10.1371/journal.pmed.1002711
10.1056/NEJMoa072100
10.1214/aos/1176344202
10.1007/978-3-319-24574-4_28
10.1148/radiol.15142062
10.1201/b16923
10.1016/0735-1097(90)90282-T
10.1161/CIRCULATIONAHA.116.023957
10.1097/RTI.0000000000000287
10.1038/s41591-018-0107-6
10.1016/j.media.2017.07.005
10.1109/ISBI.2018.8363515
10.1117/12.2216978
10.1016/j.jcct.2010.11.002
10.1016/j.jcct.2014.11.006
10.1056/NEJMoa1415516
10.1161/JAHA.115.003144
10.1056/NEJMoa1201161
10.1002/bimj.19740160113
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References LitjensGA survey on deep learning in medical image analysisMed. Image Anal.20174260882877802610.1016/j.media.2017.07.005
Writing Group Members.Heart disease and stroke statistics-2016 update: a report from the American Heart AssociationCirculation2016133e38e360
MartinSSEvaluation of a deep learning–based automated CT coronary artery calcium scoring algorithmJACC: Cardiovasc. Imaging202013524526
PokharelYAdoption of the 2013 American College of Cardiology/American Heart Association Cholesterol Management Guideline in Cardiology Practices NationwideJAMA Cardiol.2017236136928249067547041310.1001/jamacardio.2016.5922
WolterinkJMAutomatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networksMed. Image Anal.2016341231362713858410.1016/j.media.2016.04.004
HechtHS2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the Society of Cardiovascular Computed Tomography and Society of Thoracic RadiologyJ. Thorac. Imaging201732W54W662883241710.1097/RTI.0000000000000287
HosnyADeep learning for lung cancer prognostication: a retrospective multi-cohort radiomics studyPLoS Med.201815e100271130500819626908810.1371/journal.pmed.1002711
Hecht, H. S. Coronary artery calcium analysis and reporting on noncontrast chest CT scans: a paradigm shift in prevention. Curr. Cardiovasc. Imaging Reports 9 (2016).
ThanassoulisGA genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: the Framingham Heart StudyCirc. Cardiovasc. Genet.201251131211:CAS:528:DC%2BC38XltFOjtLc%3D22235037329286510.1161/CIRCGENETICS.111.961342
ThomasJDHultquistRAInterval estimation for the unbalanced case of the one-way random effects modelAnn. Stat.197865825874847020386.6205710.1214/aos/1176344202
Huo, Y. et al. Coronary calcium detection using 3D attention identical dual deep network based on weakly supervised learning. Med. Imaging 2019: Image Processinghttps://doi.org/10.1117/12.2512541 (2019).
Shadmi, R., Mazo, V., Bregman-Amitai, O. & Elnekave, E. Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (Washington, DC, 2018).
TsaoCWVasanRSCohort profile: the Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiologyInt. J. Epidemiol.2015441800181326705418515633810.1093/ije/dyv337
NihiserAJBody mass index measurement in schools.J. School Health2007776516711807641110.1111/j.1746-1561.2007.00249.x
GrundySM2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: executive summaryJ. Am. Coll. Cardiol.201973316832093042339110.1016/j.jacc.2018.11.002
D’AgostinoRBGeneral cardiovascular risk profile for use in primary care: the Framingham Heart StudyCirculation20081177437531821228510.1161/CIRCULATIONAHA.107.699579
MitchellJDPaisleyRMoonPNovakEVillinesTCCoronary artery calcium and long-term risk of death, myocardial iInfarction, and stroke: the Walter Reed Cohort StudyJACC Cardiovasc. Imaging201811179918062915357610.1016/j.jcmg.2017.09.003
HoffmannUDesign of the rule out myocardial ischemia/infarction using computer assisted tomography: a multicenter randomized comparative effectiveness trial of cardiac computed tomography versus alternative triage strategies in patients with acute chest pain in the emergency departmentAm. Heart J.2012163330338, 338.e122424002373635810.1016/j.ahj.2012.01.028
VieraAJGarrettJMUnderstanding interobserver agreement: the kappa statisticFam. Med.20053736036315883903
BudoffMJCoronary artery and thoracic calcium on noncontrast thoracic CT scans: comparison of ungated and gated examinations in patients from the COPD Gene cohortJ. Cardiovasc. Comput. Tomogr.201151131182116780610.1016/j.jcct.2010.11.002
CohenJA coefficient of agreement for nominal scalesEduc. Psychological Meas.196020374610.1177/001316446002000104
BudoffMJTen-year association of coronary artery calcium with atherosclerotic cardiovascular disease (ASCVD) events: the multi-ethnic study of atherosclerosis (MESA)Eur. Heart J.201839240124081:CAS:528:DC%2BC1MXhtlClsLbJ29688297603097510.1093/eurheartj/ehy217
PatelMRCorrection to: ACC/AATS/AHA/ASE/ASNC/SCAI/SCCT/STS 2017 appropriate use criteria for coronary revascularization in patients with stable ischemic heart diseaseJ. Nucl. Cardiol.201825219121922974887410.1007/s12350-018-1292-x
RonnebergerOFischerPBroxTU-Net: convolutional networks for biomedical image segmentation.Lecture Notes Computer Sci.2015935123424110.1007/978-3-319-24574-4_28
Kokoska, S. & Zwillinger, D. CRC Standard Probability and Statistics Tables and Formulae, Student Edition (CRC Press, 2000).
ChilesCAssociation of coronary artery calcification and mortality in the national lung screening trial: a comparison of three scoring methodsRadiology2015276822575997210.1148/radiol.15142062
RavenelJGNanceJWCoronary artery calcification in lung cancer screeningTransl. Lung Cancer Res.2018736136730050773603795910.21037/tlcr.2018.05.05
BlahaMJRole of coronary artery calcium score of zero and other negative risk markers for cardiovascular disease: the multi-ethnic study of atherosclerosis (MESA)Circulation20161338498581:CAS:528:DC%2BC28XjslClt7g%3D26801055477539110.1161/CIRCULATIONAHA.115.018524
van VelzenSGMDeep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocolsRadiology202029566793204394710.1148/radiol.2020191621
DouglasPSPROspective multicenter imaging study for evaluation of chest pain: rationale and design of the PROMISE trialAm. Heart J.2014167796803.e124890527404461710.1016/j.ahj.2014.03.003
Santini, G. et al. An automatic deep learning approach for coronary artery calcium segmentation. EMBEC & NBC 2017 374–377 https://doi.org/10.1007/978-981-10-5122-7_94 (2018).
HoffmannUMassaroJMFoxCSMandersEO’DonnellCJDefining normal distributions of coronary artery calcium in women and men (from the Framingham Heart Study)Am. J. Cardiol.200810211361141, 1141.e11:CAS:528:DC%2BD1cXht1KnsrfE18940279306537810.1016/j.amjcard.2008.06.038
HoffmannUCardiovascular event prediction and risk reclassification by coronary, aortic, and valvular calcification in the Framingham Heart StudyJ. Am. Heart Assoc.20165e00314426903006480245310.1161/JAHA.115.003144
LuMTLung cancer screening eligibility in the community: cardiovascular risk factors, coronary artery calcification, and cardiovascular eventsCirculation201613489789927647299503124610.1161/CIRCULATIONAHA.116.023957
LessmannNAutomatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutionsIEEE Trans. Med. Imaging2018376156252940878910.1109/TMI.2017.2769839
De FauwJClinically applicable deep learning for diagnosis and referral in retinal diseaseNat. Med.201824134213503010476810.1038/s41591-018-0107-61:CAS:528:DC%2BC1cXhsFSqtbzN
GuptaAThe identification of calcified coronary plaque is associated with initiation and continuation of pharmacological and lifestyle preventive therapies: a systematic review and meta-analysisJACC Cardiovasc. Imaging20171083384228797402576165110.1016/j.jcmg.2017.01.030
Wilkins, E. et al. European Cardiovascular Disease Statistics 2017. (2017).
Lessmann, N. et al. Sex differences in coronary artery and thoracic aorta calcification and their association with cardiovascular mortality in heavy smokers. JACC Cardiovasc. Imaginghttps://doi.org/10.1016/j.jcmg.2018.10.026 (2019).
DonnerAThe use of correlation and regression in the analysis of family resemblanceAm. J. Epidemiol.19791103353421:STN:280:DyaE1M3ltlSnsQ%3D%3D47456910.1093/oxfordjournals.aje.a112819
DouglasPSOutcomes of anatomical versus functional testing for coronary artery diseaseN. Engl. J. Med.2015372129113001:CAS:528:DC%2BC2MXns1CltLY%3D25773919447377310.1056/NEJMoa1415516
HuangY-LReliable categorisation of visual scoring of coronary artery calcification on low-dose CT for lung cancer screening: validation with the standard Agatston scoreEur. Radiol.201323122612332323906010.1007/s00330-012-2726-5
TakxRAPQuantification of coronary artery calcium in nongated CT to predict cardiovascular events in male lung cancer screening participants: results of the NELSON studyJ. Cardiovasc. Comput. Tomogr.2015950572553322310.1016/j.jcct.2014.11.006
HosnyAParmarCQuackenbushJSchwartzLHAertsHJWLArtificial intelligence in radiologyNat. Rev. Cancer2018185005101:CAS:528:DC%2BC1cXpvVKrsLY%3D29777175626817410.1038/s41568-018-0016-5
MukakaMMStatistics corner: A guide to appropriate use of correlation coefficient in medical researchMalawi Med. J.20122469711:STN:280:DC%2BC3sngt12kug%3D%3D236382783576830
National Lung Screening Trial Research Team et al.Reduced lung-cancer mortality with low-dose computed tomographic screeningN. Engl. J. Med201136539540910.1056/NEJMoa1102873
DetranoRCoronary calcium as a predictor of coronary events in four racial or ethnic groupsN. Engl. J. Med.2008358133613451:CAS:528:DC%2BD1cXjvFCgsrk%3D1836773610.1056/NEJMoa072100
AgatstonASQuantification of coronary artery calcium using ultrafast computed tomographyJ. Am. Coll. Cardiol.1990158278321:STN:280:DyaK3c7ntFelug%3D%3D240776210.1016/0735-1097(90)90282-T
KannelWBFeinleibMMcNamaraPMGarrisonRJCastelliWPAn investigation of coronary heart disease in families. The Framingham offspring studyAm. J. Epidemiol.19791102812901:STN:280:DyaE1M3ltlSgtQ%3D%3D47456510.1093/oxfordjournals.aje.a112813
Qazi, S. et al. Increased aortic diameters on multidetector computed tomographic scan are independent predictors of incident adverse cardiovascular events: the Framingham Heart Study. Circ. Cardiovasc. Imaging10, e006776 (2017).
de Vos, B. D. et al. Direct automatic coronary calcium scoring in cardiac and chest CT. IEEE Trans. Med. Imaginghttps://doi.org/10.1109/TMI.2019.2899534 (2019).
RaffGLSCCT guidelines on the use of coronary computed tomographic angiography for patients presenting with acute chest pain to the emergency department: a report of
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References_xml – reference: HosnyAParmarCQuackenbushJSchwartzLHAertsHJWLArtificial intelligence in radiologyNat. Rev. Cancer2018185005101:CAS:528:DC%2BC1cXpvVKrsLY%3D29777175626817410.1038/s41568-018-0016-5
– reference: BudoffMJTen-year association of coronary artery calcium with atherosclerotic cardiovascular disease (ASCVD) events: the multi-ethnic study of atherosclerosis (MESA)Eur. Heart J.201839240124081:CAS:528:DC%2BC1MXhtlClsLbJ29688297603097510.1093/eurheartj/ehy217
– reference: HosnyADeep learning for lung cancer prognostication: a retrospective multi-cohort radiomics studyPLoS Med.201815e100271130500819626908810.1371/journal.pmed.1002711
– reference: CohenJA coefficient of agreement for nominal scalesEduc. Psychological Meas.196020374610.1177/001316446002000104
– reference: DouglasPSOutcomes of anatomical versus functional testing for coronary artery diseaseN. Engl. J. Med.2015372129113001:CAS:528:DC%2BC2MXns1CltLY%3D25773919447377310.1056/NEJMoa1415516
– reference: ChilesCAssociation of coronary artery calcification and mortality in the national lung screening trial: a comparison of three scoring methodsRadiology2015276822575997210.1148/radiol.15142062
– reference: RaffGLSCCT guidelines on the use of coronary computed tomographic angiography for patients presenting with acute chest pain to the emergency department: a report of the Society of Cardiovascular Computed Tomography Guidelines CommitteeJ. Cardiovasc. Comput. Tomogr.201482542712515191810.1016/j.jcct.2014.06.002
– reference: LuMTLung cancer screening eligibility in the community: cardiovascular risk factors, coronary artery calcification, and cardiovascular eventsCirculation201613489789927647299503124610.1161/CIRCULATIONAHA.116.023957
– reference: HoffmannUCardiovascular event prediction and risk reclassification by coronary, aortic, and valvular calcification in the Framingham Heart StudyJ. Am. Heart Assoc.20165e00314426903006480245310.1161/JAHA.115.003144
– reference: Writing Group Members.Heart disease and stroke statistics-2016 update: a report from the American Heart AssociationCirculation2016133e38e360
– reference: van VelzenSGMDeep learning for automatic calcium scoring in CT: validation using multiple cardiac CT and chest CT protocolsRadiology202029566793204394710.1148/radiol.2020191621
– reference: PatelMRCorrection to: ACC/AATS/AHA/ASE/ASNC/SCAI/SCCT/STS 2017 appropriate use criteria for coronary revascularization in patients with stable ischemic heart diseaseJ. Nucl. Cardiol.201825219121922974887410.1007/s12350-018-1292-x
– reference: Lessmann, N. et al. Sex differences in coronary artery and thoracic aorta calcification and their association with cardiovascular mortality in heavy smokers. JACC Cardiovasc. Imaginghttps://doi.org/10.1016/j.jcmg.2018.10.026 (2019).
– reference: TsaoCWVasanRSCohort profile: the Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiologyInt. J. Epidemiol.2015441800181326705418515633810.1093/ije/dyv337
– reference: NihiserAJBody mass index measurement in schools.J. School Health2007776516711807641110.1111/j.1746-1561.2007.00249.x
– reference: MukakaMMStatistics corner: A guide to appropriate use of correlation coefficient in medical researchMalawi Med. J.20122469711:STN:280:DC%2BC3sngt12kug%3D%3D236382783576830
– reference: ThomasJDHultquistRAInterval estimation for the unbalanced case of the one-way random effects modelAnn. Stat.197865825874847020386.6205710.1214/aos/1176344202
– reference: BudoffMJCoronary artery and thoracic calcium on noncontrast thoracic CT scans: comparison of ungated and gated examinations in patients from the COPD Gene cohortJ. Cardiovasc. Comput. Tomogr.201151131182116780610.1016/j.jcct.2010.11.002
– reference: MartinSSEvaluation of a deep learning–based automated CT coronary artery calcium scoring algorithmJACC: Cardiovasc. Imaging202013524526
– reference: TakxRAPQuantification of coronary artery calcium in nongated CT to predict cardiovascular events in male lung cancer screening participants: results of the NELSON studyJ. Cardiovasc. Comput. Tomogr.2015950572553322310.1016/j.jcct.2014.11.006
– reference: Huo, Y. et al. Coronary calcium detection using 3D attention identical dual deep network based on weakly supervised learning. Med. Imaging 2019: Image Processinghttps://doi.org/10.1117/12.2512541 (2019).
– reference: GrundySM2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: executive summaryJ. Am. Coll. Cardiol.201973316832093042339110.1016/j.jacc.2018.11.002
– reference: AgatstonASQuantification of coronary artery calcium using ultrafast computed tomographyJ. Am. Coll. Cardiol.1990158278321:STN:280:DyaK3c7ntFelug%3D%3D240776210.1016/0735-1097(90)90282-T
– reference: DetranoRCoronary calcium as a predictor of coronary events in four racial or ethnic groupsN. Engl. J. Med.2008358133613451:CAS:528:DC%2BD1cXjvFCgsrk%3D1836773610.1056/NEJMoa072100
– reference: HoffmannUMassaroJMFoxCSMandersEO’DonnellCJDefining normal distributions of coronary artery calcium in women and men (from the Framingham Heart Study)Am. J. Cardiol.200810211361141, 1141.e11:CAS:528:DC%2BD1cXht1KnsrfE18940279306537810.1016/j.amjcard.2008.06.038
– reference: AhrensHSearleSRLinear Models. John Wiley & Sons, Inc., New York-London-Sydney-Toronto 1971. XXI, 532 S. $9.50Biometrische Z.197416787910.1002/bimj.19740160113
– reference: Lessmann, N. et al. Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT. Med. Imaging 2016: Computer-Aided Diagnosishttps://doi.org/10.1117/12.2216978 (2016).
– reference: DonnerAThe use of correlation and regression in the analysis of family resemblanceAm. J. Epidemiol.19791103353421:STN:280:DyaE1M3ltlSnsQ%3D%3D47456910.1093/oxfordjournals.aje.a112819
– reference: ThanassoulisGA genetic risk score is associated with incident cardiovascular disease and coronary artery calcium: the Framingham Heart StudyCirc. Cardiovasc. Genet.201251131211:CAS:528:DC%2BC38XltFOjtLc%3D22235037329286510.1161/CIRCGENETICS.111.961342
– reference: Qazi, S. et al. Increased aortic diameters on multidetector computed tomographic scan are independent predictors of incident adverse cardiovascular events: the Framingham Heart Study. Circ. Cardiovasc. Imaging10, e006776 (2017).
– reference: RavenelJGNanceJWCoronary artery calcification in lung cancer screeningTransl. Lung Cancer Res.2018736136730050773603795910.21037/tlcr.2018.05.05
– reference: Wilkins, E. et al. European Cardiovascular Disease Statistics 2017. (2017).
– reference: KannelWBFeinleibMMcNamaraPMGarrisonRJCastelliWPAn investigation of coronary heart disease in families. The Framingham offspring studyAm. J. Epidemiol.19791102812901:STN:280:DyaE1M3ltlSgtQ%3D%3D47456510.1093/oxfordjournals.aje.a112813
– reference: LitjensGA survey on deep learning in medical image analysisMed. Image Anal.20174260882877802610.1016/j.media.2017.07.005
– reference: National Lung Screening Trial Research Team et al.Reduced lung-cancer mortality with low-dose computed tomographic screeningN. Engl. J. Med201136539540910.1056/NEJMoa1102873
– reference: Hecht, H. S. Coronary artery calcium analysis and reporting on noncontrast chest CT scans: a paradigm shift in prevention. Curr. Cardiovasc. Imaging Reports 9 (2016).
– reference: DouglasPSPROspective multicenter imaging study for evaluation of chest pain: rationale and design of the PROMISE trialAm. Heart J.2014167796803.e124890527404461710.1016/j.ahj.2014.03.003
– reference: Shadmi, R., Mazo, V., Bregman-Amitai, O. & Elnekave, E. Fully-convolutional deep-learning based system for coronary calcium score prediction from non-contrast chest CT. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (Washington, DC, 2018).
– reference: HuangY-LReliable categorisation of visual scoring of coronary artery calcification on low-dose CT for lung cancer screening: validation with the standard Agatston scoreEur. Radiol.201323122612332323906010.1007/s00330-012-2726-5
– reference: PoplinRPrediction of cardiovascular risk factors from retinal fundus photographs via deep learningNat. Biomed. Eng.201821581643101571310.1038/s41551-018-0195-0
– reference: HoffmannUCoronary CT angiography versus standard evaluation in acute chest painN. Engl. J. Med.20123672993081:CAS:528:DC%2BC38XhtFegsrrF22830462366221710.1056/NEJMoa1201161
– reference: WolterinkJMAutomatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networksMed. Image Anal.2016341231362713858410.1016/j.media.2016.04.004
– reference: Kokoska, S. & Zwillinger, D. CRC Standard Probability and Statistics Tables and Formulae, Student Edition (CRC Press, 2000).
– reference: BlahaMJRole of coronary artery calcium score of zero and other negative risk markers for cardiovascular disease: the multi-ethnic study of atherosclerosis (MESA)Circulation20161338498581:CAS:528:DC%2BC28XjslClt7g%3D26801055477539110.1161/CIRCULATIONAHA.115.018524
– reference: RonnebergerOFischerPBroxTU-Net: convolutional networks for biomedical image segmentation.Lecture Notes Computer Sci.2015935123424110.1007/978-3-319-24574-4_28
– reference: HoffmannUDesign of the rule out myocardial ischemia/infarction using computer assisted tomography: a multicenter randomized comparative effectiveness trial of cardiac computed tomography versus alternative triage strategies in patients with acute chest pain in the emergency departmentAm. Heart J.2012163330338, 338.e122424002373635810.1016/j.ahj.2012.01.028
– reference: HechtHS2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans: a report of the Society of Cardiovascular Computed Tomography and Society of Thoracic RadiologyJ. Thorac. Imaging201732W54W662883241710.1097/RTI.0000000000000287
– reference: MitchellJDPaisleyRMoonPNovakEVillinesTCCoronary artery calcium and long-term risk of death, myocardial iInfarction, and stroke: the Walter Reed Cohort StudyJACC Cardiovasc. Imaging201811179918062915357610.1016/j.jcmg.2017.09.003
– reference: Santini, G. et al. An automatic deep learning approach for coronary artery calcium segmentation. EMBEC & NBC 2017 374–377 https://doi.org/10.1007/978-981-10-5122-7_94 (2018).
– reference: SplanskyGLThe third generation cohort of the National Heart, Lung, and Blood Institute’s Framingham Heart Study: design, recruitment, and initial examinationAm. J. Epidemiol.2007165132813351737218910.1093/aje/kwm021
– reference: LessmannNAutomatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutionsIEEE Trans. Med. Imaging2018376156252940878910.1109/TMI.2017.2769839
– reference: PokharelYAdoption of the 2013 American College of Cardiology/American Heart Association Cholesterol Management Guideline in Cardiology Practices NationwideJAMA Cardiol.2017236136928249067547041310.1001/jamacardio.2016.5922
– reference: De FauwJClinically applicable deep learning for diagnosis and referral in retinal diseaseNat. Med.201824134213503010476810.1038/s41591-018-0107-61:CAS:528:DC%2BC1cXhsFSqtbzN
– reference: de Vos, B. D. et al. Direct automatic coronary calcium scoring in cardiac and chest CT. IEEE Trans. Med. Imaginghttps://doi.org/10.1109/TMI.2019.2899534 (2019).
– reference: GuptaAThe identification of calcified coronary plaque is associated with initiation and continuation of pharmacological and lifestyle preventive therapies: a systematic review and meta-analysisJACC Cardiovasc. Imaging20171083384228797402576165110.1016/j.jcmg.2017.01.030
– reference: VieraAJGarrettJMUnderstanding interobserver agreement: the kappa statisticFam. Med.20053736036315883903
– reference: D’AgostinoRBGeneral cardiovascular risk profile for use in primary care: the Framingham Heart StudyCirculation20081177437531821228510.1161/CIRCULATIONAHA.107.699579
– volume: 34
  start-page: 123
  year: 2016
  ident: 20966_CR34
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2016.04.004
– volume: 7
  start-page: 361
  year: 2018
  ident: 20966_CR11
  publication-title: Transl. Lung Cancer Res.
  doi: 10.21037/tlcr.2018.05.05
– volume: 23
  start-page: 1226
  year: 2013
  ident: 20966_CR30
  publication-title: Eur. Radiol.
  doi: 10.1007/s00330-012-2726-5
– volume: 10
  start-page: 833
  year: 2017
  ident: 20966_CR12
  publication-title: JACC Cardiovasc. Imaging
  doi: 10.1016/j.jcmg.2017.01.030
– ident: 20966_CR35
  doi: 10.1016/j.jcmg.2018.10.026
– volume: 117
  start-page: 743
  year: 2008
  ident: 20966_CR17
  publication-title: Circulation
  doi: 10.1161/CIRCULATIONAHA.107.699579
– volume: 163
  start-page: 330
  year: 2012
  ident: 20966_CR20
  publication-title: Am. Heart J.
  doi: 10.1016/j.ahj.2012.01.028
– volume: 25
  start-page: 2191
  year: 2018
  ident: 20966_CR9
  publication-title: J. Nucl. Cardiol.
  doi: 10.1007/s12350-018-1292-x
– volume: 37
  start-page: 615
  year: 2018
  ident: 20966_CR37
  publication-title: IEEE Trans. Med. Imaging
  doi: 10.1109/TMI.2017.2769839
– volume: 73
  start-page: 3168
  year: 2019
  ident: 20966_CR7
  publication-title: J. Am. Coll. Cardiol.
  doi: 10.1016/j.jacc.2018.11.002
– volume: 37
  start-page: 360
  year: 2005
  ident: 20966_CR23
  publication-title: Fam. Med.
– volume: 295
  start-page: 66
  year: 2020
  ident: 20966_CR41
  publication-title: Radiology
  doi: 10.1148/radiol.2020191621
– volume: 5
  start-page: 113
  year: 2012
  ident: 20966_CR6
  publication-title: Circ. Cardiovasc. Genet.
  doi: 10.1161/CIRCGENETICS.111.961342
– volume: 165
  start-page: 1328
  year: 2007
  ident: 20966_CR49
  publication-title: Am. J. Epidemiol.
  doi: 10.1093/aje/kwm021
– volume: 20
  start-page: 37
  year: 1960
  ident: 20966_CR56
  publication-title: Educ. Psychological Meas.
  doi: 10.1177/001316446002000104
– volume: 8
  start-page: 254
  year: 2014
  ident: 20966_CR10
  publication-title: J. Cardiovasc. Comput. Tomogr.
  doi: 10.1016/j.jcct.2014.06.002
– volume: 102
  start-page: 1136
  year: 2008
  ident: 20966_CR21
  publication-title: Am. J. Cardiol.
  doi: 10.1016/j.amjcard.2008.06.038
– volume: 77
  start-page: 651
  year: 2007
  ident: 20966_CR57
  publication-title: J. School Health
  doi: 10.1111/j.1746-1561.2007.00249.x
– volume: 133
  start-page: 849
  year: 2016
  ident: 20966_CR24
  publication-title: Circulation
  doi: 10.1161/CIRCULATIONAHA.115.018524
– volume: 44
  start-page: 1800
  year: 2015
  ident: 20966_CR44
  publication-title: Int. J. Epidemiol.
  doi: 10.1093/ije/dyv337
– volume: 2
  start-page: 361
  year: 2017
  ident: 20966_CR3
  publication-title: JAMA Cardiol.
  doi: 10.1001/jamacardio.2016.5922
– volume: 110
  start-page: 335
  year: 1979
  ident: 20966_CR54
  publication-title: Am. J. Epidemiol.
  doi: 10.1093/oxfordjournals.aje.a112819
– ident: 20966_CR38
  doi: 10.1117/12.2512541
– volume: 13
  start-page: 524
  year: 2020
  ident: 20966_CR42
  publication-title: JACC: Cardiovasc. Imaging
– volume: 167
  start-page: 796
  year: 2014
  ident: 20966_CR19
  publication-title: Am. Heart J.
  doi: 10.1016/j.ahj.2014.03.003
– volume: 24
  start-page: 69
  year: 2012
  ident: 20966_CR22
  publication-title: Malawi Med. J.
– ident: 20966_CR51
  doi: 10.1161/CIRCIMAGING.117.006776
– ident: 20966_CR39
  doi: 10.1007/978-981-10-5122-7_94
– volume: 18
  start-page: 500
  year: 2018
  ident: 20966_CR13
  publication-title: Nat. Rev. Cancer
  doi: 10.1038/s41568-018-0016-5
– volume: 39
  start-page: 2401
  year: 2018
  ident: 20966_CR26
  publication-title: Eur. Heart J.
  doi: 10.1093/eurheartj/ehy217
– volume: 133
  start-page: e38
  year: 2016
  ident: 20966_CR2
  publication-title: Circulation
– volume: 11
  start-page: 1799
  year: 2018
  ident: 20966_CR27
  publication-title: JACC Cardiovasc. Imaging
  doi: 10.1016/j.jcmg.2017.09.003
– ident: 20966_CR28
  doi: 10.1007/s12410-016-9372-2
– volume: 110
  start-page: 281
  year: 1979
  ident: 20966_CR48
  publication-title: Am. J. Epidemiol.
  doi: 10.1093/oxfordjournals.aje.a112813
– volume: 365
  start-page: 395
  year: 2011
  ident: 20966_CR18
  publication-title: N. Engl. J. Med
  doi: 10.1056/NEJMoa1102873
– ident: 20966_CR36
  doi: 10.1109/TMI.2019.2899534
– volume: 2
  start-page: 158
  year: 2018
  ident: 20966_CR4
  publication-title: Nat. Biomed. Eng.
  doi: 10.1038/s41551-018-0195-0
– volume: 15
  start-page: e1002711
  year: 2018
  ident: 20966_CR15
  publication-title: PLoS Med.
  doi: 10.1371/journal.pmed.1002711
– volume: 358
  start-page: 1336
  year: 2008
  ident: 20966_CR47
  publication-title: N. Engl. J. Med.
  doi: 10.1056/NEJMoa072100
– volume: 6
  start-page: 582
  year: 1978
  ident: 20966_CR55
  publication-title: Ann. Stat.
  doi: 10.1214/aos/1176344202
– volume: 9351
  start-page: 234
  year: 2015
  ident: 20966_CR50
  publication-title: Lecture Notes Computer Sci.
  doi: 10.1007/978-3-319-24574-4_28
– volume: 276
  start-page: 82
  year: 2015
  ident: 20966_CR33
  publication-title: Radiology
  doi: 10.1148/radiol.15142062
– ident: 20966_CR52
  doi: 10.1201/b16923
– ident: 20966_CR1
– volume: 15
  start-page: 827
  year: 1990
  ident: 20966_CR5
  publication-title: J. Am. Coll. Cardiol.
  doi: 10.1016/0735-1097(90)90282-T
– volume: 134
  start-page: 897
  year: 2016
  ident: 20966_CR29
  publication-title: Circulation
  doi: 10.1161/CIRCULATIONAHA.116.023957
– volume: 32
  start-page: W54
  year: 2017
  ident: 20966_CR8
  publication-title: J. Thorac. Imaging
  doi: 10.1097/RTI.0000000000000287
– volume: 24
  start-page: 1342
  year: 2018
  ident: 20966_CR14
  publication-title: Nat. Med.
  doi: 10.1038/s41591-018-0107-6
– volume: 42
  start-page: 60
  year: 2017
  ident: 20966_CR16
  publication-title: Med. Image Anal.
  doi: 10.1016/j.media.2017.07.005
– ident: 20966_CR40
  doi: 10.1109/ISBI.2018.8363515
– ident: 20966_CR43
  doi: 10.1117/12.2216978
– volume: 5
  start-page: 113
  year: 2011
  ident: 20966_CR31
  publication-title: J. Cardiovasc. Comput. Tomogr.
  doi: 10.1016/j.jcct.2010.11.002
– volume: 9
  start-page: 50
  year: 2015
  ident: 20966_CR32
  publication-title: J. Cardiovasc. Comput. Tomogr.
  doi: 10.1016/j.jcct.2014.11.006
– volume: 372
  start-page: 1291
  year: 2015
  ident: 20966_CR45
  publication-title: N. Engl. J. Med.
  doi: 10.1056/NEJMoa1415516
– volume: 5
  start-page: e003144
  year: 2016
  ident: 20966_CR25
  publication-title: J. Am. Heart Assoc.
  doi: 10.1161/JAHA.115.003144
– volume: 367
  start-page: 299
  year: 2012
  ident: 20966_CR46
  publication-title: N. Engl. J. Med.
  doi: 10.1056/NEJMoa1201161
– volume: 16
  start-page: 78
  year: 1974
  ident: 20966_CR53
  publication-title: Biometrische Z.
  doi: 10.1002/bimj.19740160113
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Snippet Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this...
Coronary artery calcium is an accurate predictor of cardiovascular events but this information is not routinely quantified. Here the authors show a robust and...
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SubjectTerms 631/114/1305
639/705/117
692/308/409
692/4019/2776
692/499
Aged
Arteriosclerosis
Artificial neural networks
Asymptomatic Diseases
Automation
Biomarkers
Calcification (ectopic)
Calcium
Calcium - analysis
Cardiovascular diseases
Cardiovascular Diseases - complications
Cardiovascular Diseases - diagnosis
Cardiovascular Diseases - epidemiology
Cardiovascular Diseases - pathology
Chest
Chest Pain - diagnosis
Chest Pain - etiology
Computed tomography
Coronary artery
Coronary vessels
Coronary Vessels - diagnostic imaging
Coronary Vessels - pathology
Deep Learning
Female
Follow-Up Studies
Health risks
Heart Disease Risk Factors
Humanities and Social Sciences
Humans
Image Processing, Computer-Assisted - methods
Male
Middle Aged
multidisciplinary
Neural networks
Reproducibility of Results
Retrospective Studies
Risk analysis
Risk Assessment - methods
Risk factors
Robustness
Science
Science (multidisciplinary)
Tomography
Tomography, X-Ray Computed
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Title Deep convolutional neural networks to predict cardiovascular risk from computed tomography
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Volume 12
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