{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:57:57Z","timestamp":1787497077300,"version":"build-2736575974"},"reference-count":45,"publisher":"Japan Atherosclerosis Society","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAT","J Atheroscler Thromb"],"published-print":{"date-parts":[[2022,5,1]]},"DOI":"10.5551\/jat.62059","type":"journal-article","created":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T18:08:35Z","timestamp":1616090915000},"page":"579-596","source":"Crossref","is-referenced-by-count":22,"title":["Development and Validation of Retinal Vasculature Nomogram in Suspected Angina Due to Coronary Artery Disease"],"prefix":"10.5551","volume":"29","author":[{"given":"Pingting","family":"Zhong","sequence":"first","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"},{"name":"Shantou University Medical College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixi","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Guangdong Geriatrics Institute, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingsheng","family":"Peng","sequence":"additional","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"},{"name":"Shantou University Medical College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manqing","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingwen","family":"Lin","sequence":"additional","affiliation":[{"name":"Shantou University Medical College"},{"name":"Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"},{"name":"Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"},{"name":"School of Medicine, South China University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaowei","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"},{"name":"Southern Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Kuang","sequence":"additional","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirong","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honghua","family":"Yu","sequence":"additional","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaiyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3489","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"1) Gandhi MM, Lampe FC, Wood DA. Incidence, clinical characteristics, and short-term prognosis of angina pectoris. Br Heart J, 1995; 73: 193-198","DOI":"10.1136\/hrt.73.2.193"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2) Kravitz RL, Laouri M, Kahan JP, Guzy P, Sherman T, Hilborne L, Brook RH. Validity of criteria used for detecting underuse of coronary revascularization. JAMA, 1995; 274: 632-638","DOI":"10.1001\/jama.274.8.632"},{"key":"3","doi-asserted-by":"crossref","unstructured":"3) Sekhri N, Feder GS, Junghans C, Hemingway H, Timmis AD. How effective are rapid access chest pain clinics? Prognosis of incident angina and non-cardiac chest pain in 8762 consecutive patients. Heart, 2007; 93: 458-463","DOI":"10.1136\/hrt.2006.090894"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4) Douglas PS, Hoffmann U, Patel MR, Mark DB, Al-Khalidi HR, Cavanaugh B, Cole J, Dolor RJ, Fordyce CB, Huang M, Khan MA, Kosinski AS, Krucoff MW, Malhotra V, Picard MH, Udelson JE, Velazquez EJ, Yow E, Cooper LS, Lee KL. Outcomes of anatomical versus functional testing for coronary artery disease. N. Engl. J. Med., 2015; 372: 1291-1300","DOI":"10.1056\/NEJMoa1415516"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5) Genders TS, Steyerberg EW, Alkadhi H, Leschka S, Desbiolles L, Nieman K, Galema TW, Meijboom WB, Mollet NR, de Feyter PJ, Cademartiri F, Maffei E, Dewey M, Zimmermann E, Laule M, Pugliese F, Barbagallo R, Sinitsyn V, Bogaert J, Goetschalckx K, Schoepf UJ, Rowe GW, Schuijf JD, Bax JJ, de Graaf FR, Knuuti J, Kajander S, van Mieghem CA, Meijs MF, Cramer MJ, Gopalan D, Feuchtner G, Friedrich G, Krestin GP, Hunink MG. A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension. Eur. Heart J., 2011; 32: 1316-1330","DOI":"10.1093\/eurheartj\/ehr014"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6) Bittencourt MS, Hulten E, Polonsky TS, Hoffman U, Nasir K, Abbara S, Di Carli M, Blankstein R. European Society of Cardiology-Recommended Coronary Artery Disease Consortium Pretest Probability Scores More Accurately Predict Obstructive Coronary Disease and Cardiovascular Events Than the Diamond and Forrester Score: The Partners Registry. Circulation, 2016; 134: 201-211","DOI":"10.1161\/CIRCULATIONAHA.116.023396"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7) Bhargava M, Ikram MK, Wong TY. How does hypertension affect your eyes? J Hum Hypertens, 2012; 26: 71-83","DOI":"10.1038\/jhh.2011.37"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8) Flammer J, Konieczka K, Bruno RM, Virdis A, Flammer AJ, Taddei S. The eye and the heart. Eur. Heart J., 2013; 34: 1270-1278","DOI":"10.1093\/eurheartj\/eht023"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9) Grassi G, Buzzi S, Dell\u2019Oro R, Mineo C, Dimitriadis K, Seravalle G, Lonati L, Cuspidi C. Structural alterations of the retinal microcirculation in the \u201cprehypertensive\u201d high- normal blood pressure state. Curr. Pharm. Des., 2013; 19: 2375-2381","DOI":"10.2174\/1381612811319130005"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10) Wong TY. Is retinal photography useful in the measurement of stroke risk? Lancet Neurol, 2004; 3: 179-183","DOI":"10.1016\/S1474-4422(04)00682-9"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11) N\u00e4gele MP, Barthelmes J, Ludovici V, Cantatore S, von Eckardstein A, Enseleit F, L\u00fcscher TF, Ruschitzka F, Sudano I, Flammer AJ. Retinal microvascular dysfunction in heart failure. Eur. Heart J., 2018; 39: 47-56","DOI":"10.1093\/eurheartj\/ehx565"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12) Cheung CY, Ikram MK, Sabanayagam C, Wong TY. Retinal microvasculature as a model to study the manifestations of hypertension. Hypertension, 2012; 60: 1094-1103","DOI":"10.1161\/HYPERTENSIONAHA.111.189142"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13) Doubal FN, MacGillivray TJ, Hokke PE, Dhillon B, Dennis MS, Wardlaw JM. Differences in retinal vessels support a distinct vasculopathy causing lacunar stroke. Neurology, 2009; 72: 1773-1778","DOI":"10.1212\/WNL.0b013e3181a60a71"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14) Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng, 2018; 2: 158-164","DOI":"10.1038\/s41551-018-0195-0"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15) Dai G, He W, Xu L, Pazo EE, Lin T, Liu S, Zhang C. Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population. PLoS ONE, 2020; 15: e0230111","DOI":"10.1371\/journal.pone.0230111"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16) Zhang L, Yuan M, An Z, Zhao X, Wu H, Li H, Wang Y, Sun B, Li H, Ding S, Zeng X, Chao L, Li P, Wu W. Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China. PLoS ONE, 2020; 15: e0233166","DOI":"10.1371\/journal.pone.0233166"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17) Taqueti VR, Di Carli MF. Coronary Microvascular Disease Pathogenic Mechanisms and Therapeutic Options: JACC State-of-the-Art Review. J. Am. Coll. Cardiol., 2018; 72: 2625-2641","DOI":"10.1016\/j.jacc.2018.09.042"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18) Gupta A, Taqueti VR, van de Hoef TP, Bajaj NS, Bravo PE, Murthy VL, Osborne MT, Seidelmann SB, Vita T, Bibbo CF, Harrington M, Hainer J, Rimoldi O, Dorbala S, Bhatt DL, Blankstein R, Camici PG, Di Carli MF. Integrated Noninvasive Physiological Assessment of Coronary Circulatory Function and Impact on Cardiovascular Mortality in Patients With Stable Coronary Artery Disease. Circulation, 2017; 136: 2325-2336","DOI":"10.1161\/CIRCULATIONAHA.117.029992"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19) Catapano AL, Graham I, De Backer G, Wiklund O, Chapman MJ, Drexel H, Hoes AW, Jennings CS, Landmesser U, Pedersen TR, Reiner \u017d, Riccardi G, Taskinen MR, Tokgozoglu L, Verschuren WMM, Vlachopoulos C, Wood DA, Zamorano JL, Cooney MT. 2016 ESC\/EAS Guidelines for the Management of Dyslipidaemias. Eur. Heart J., 2016; 37: 2999-3058","DOI":"10.1093\/eurheartj\/ehw272"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20) Spaide RF, Klancnik JM, Cooney MJ. Retinal vascular layers imaged by fluorescein angiography and optical coherence tomography angiography. JAMA Ophthalmol, 2015; 133: 45-50","DOI":"10.1001\/jamaophthalmol.2014.3616"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21) Agemy SA, Scripsema NK, Shah CM, Chui T, Garcia PM, Lee JG, Gentile RC, Hsiao YS, Zhou Q, Ko T, Rosen RB. Retinal Vascular Perfusion Density Mapping Using Optical Coherence Tomography Angiography in Normals and Diabetic Retinopathy Patients. Retina, 2015; 35: 2353-2363","DOI":"10.1097\/IAE.0000000000000862"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22) Motaghiannezam R, Fraser S. Logarithmic intensity and speckle-based motion contrast methods for human retinal vasculature visualization using swept source optical coherence tomography. Biomed Opt Express, 2012; 3: 503-521","DOI":"10.1364\/BOE.3.000503"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23) Ambler G, Seaman S, Omar RZ. An evaluation of penalised survival methods for developing prognostic models with rare events. Stat Med, 2012; 31: 1150-1161","DOI":"10.1002\/sim.4371"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24) Deo RC. Machine Learning in Medicine. Circulation, 2015; 132: 1920-1930","DOI":"10.1161\/CIRCULATIONAHA.115.001593"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25) Waljee AK, Higgins PD. Machine learning in medicine: a primer for physicians. Am. J. Gastroenterol., 2010; 105: 1224-1226","DOI":"10.1038\/ajg.2010.173"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26) Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Andreini D, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJ, Cury RC, Delago A, Gomez M, Gransar H, Hadamitzky M, Hausleiter J, Hindoyan N, Feuchtner G, Kaufmann PA, Kim YJ, Leipsic J, Lin FY, Maffei E, Marques H, Pontone G, Raff G, Rubinshtein R, Shaw LJ, Stehli J, Villines TC, Dunning A, Min JK, Slomka PJ. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur. Heart J., 2017; 38: 500-507","DOI":"10.1093\/eurheartj\/ehw188"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27) van Rosendael AR, Maliakal G, Kolli KK, Beecy A, Al\u2019Aref SJ, Dwivedi A, Singh G, Panday M, Kumar A, Ma X, Achenbach S, Al-Mallah MH, Andreini D, Bax JJ, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJW, Cury RC, DeLago A, Feuchtner G, Hadamitzky M, Hausleiter J, Kaufmann PA, Kim YJ, Leipsic JA, Maffei E, Marques H, Pontone G, Raff GL, Rubinshtein R, Shaw LJ, Villines TC, Gransar H, Lu Y, Jones EC, Pe\u00f1a JM, Lin FY, Min JK. Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry. J Cardiovasc Comput Tomogr, 2018; 12: 204-209","DOI":"10.1016\/j.jcct.2018.04.011"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28) Chang J, Ko A, Park SM, Choi S, Kim K, Kim SM, Yun JM, Kang U, Shin IH, Shin JY, Ko T, Lee J, Oh BL, Park KH. Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images. Am. J. Ophthalmol., 2020","DOI":"10.1016\/j.ajo.2020.03.027"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29) Seidelmann SB, Claggett B, Bravo PE, Gupta A, Farhad H, Klein BE, Klein R, Di Carli M, Solomon SD. Retinal Vessel Calibers in Predicting Long-Term Cardiovascular Outcomes: The Atherosclerosis Risk in Communities Study. Circulation., 2016; 134: 1328-1338","DOI":"10.1161\/CIRCULATIONAHA.116.023425"},{"key":"30","doi-asserted-by":"crossref","unstructured":"30) Gopinath B, Chiha J, Plant AJ, Thiagalingam A, Burlutsky G, Kovoor P, Liew G, Mitchell P. Associations between retinal microvascular structure and the severity and extent of coronary artery disease. Atherosclerosis., 2014; 236(1): 25-30","DOI":"10.1016\/j.atherosclerosis.2014.06.018"},{"key":"31","doi-asserted-by":"crossref","unstructured":"31) Arnould L, Guenancia C, Azemar A, Alan G, Pitois S, Bichat F, Zeller M, Gabrielle PH, Bron AM, Creuzot-Garcher C, Cottin Y. The EYE-MI Pilot Study: A Prospective Acute Coronary Syndrome Cohort Evaluated With Retinal Optical Coherence Tomography Angiography. Invest Ophthalmol Vis Sci., 2018; 59(10): 4299-4306","DOI":"10.1167\/iovs.18-24090"},{"key":"32","doi-asserted-by":"crossref","unstructured":"32) Wang J, Jiang J, Zhang Y, Qian YW, Zhang JF, Wang ZL. Retinal and choroidal vascular changes in coronary heart disease: an optical coherence tomography angiography study. Biomed Opt Express., 2019; 10(4): 1532-1544","DOI":"10.1364\/BOE.10.001532"},{"key":"33","doi-asserted-by":"crossref","unstructured":"33) Tabatabaee A, Asharin MR, Dehghan MH, Pourbehi MR, Nasiri-Ahmadabadi M, Assadi M. Retinal vessel abnormalities predict coronary artery diseases. Perfusion, 2013; 28: 232-237","DOI":"10.1177\/0267659112473173"},{"key":"34","doi-asserted-by":"crossref","unstructured":"34) Wong TY, Klein R, Sharrett AR, Duncan BB, Couper DJ, Tielsch JM, Klein BE, Hubbard LD. Retinal arteriolar narrowing and risk of coronary heart disease in men and women. The Atherosclerosis Risk in Communities Study. JAMA, 2002; 287: 1153-1159","DOI":"10.1001\/jama.287.9.1153"},{"key":"35","doi-asserted-by":"crossref","unstructured":"35) Anderson TJ, Gerhard MD, Meredith IT, Charbonneau F, Delagrange D, Creager MA, Selwyn AP, Ganz P. Systemic nature of endothelial dysfunction in atherosclerosis. Am. J. Cardiol., 1995; 75: 71B-74B","DOI":"10.1016\/0002-9149(95)80017-M"},{"key":"36","doi-asserted-by":"crossref","unstructured":"36) Anderson TJ, Uehata A, Gerhard MD, Meredith IT, Knab S, Delagrange D, Lieberman EH, Ganz P, Creager MA, Yeung AC. Close relation of endothelial function in the human coronary and peripheral circulations. J. Am. Coll. Cardiol., 1995; 26: 1235-1241","DOI":"10.1016\/0735-1097(95)00327-4"},{"key":"37","doi-asserted-by":"crossref","unstructured":"37) Singh T, Bing R, Dweck MR, van Beek EJR, Mills NL, Williams MC, Villines TC, Newby DE, Adamson PD. Exercise Electrocardiography and Computed Tomography Coronary Angiography for Patients With Suspected Stable Angina Pectoris: A Post Hoc Analysis of the Randomized SCOT-HEART Trial. JAMA Cardiol, 2020","DOI":"10.1001\/jamacardio.2020.1567"},{"key":"38","doi-asserted-by":"crossref","unstructured":"38) Sekhri N, Feder GS, Junghans C, Eldridge S, Umaipalan A, Madhu R, Hemingway H, Timmis AD. Incremental prognostic value of the exercise electrocardiogram in the initial assessment of patients with suspected angina: cohort study. BMJ, 2008; 337: a2240","DOI":"10.1136\/bmj.a2240"},{"key":"39","doi-asserted-by":"crossref","unstructured":"39) Lapuschkin S, W\u00e4ldchen S, Binder A, Montavon G, Samek W, M\u00fcller KR. Unmasking Clever Hans predictors and assessing what machines really learn. Nat Commun, 2019; 10: 1096","DOI":"10.1038\/s41467-019-08987-4"},{"key":"40","doi-asserted-by":"crossref","unstructured":"40) LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015; 521: 436-444","DOI":"10.1038\/nature14539"},{"key":"41","doi-asserted-by":"crossref","unstructured":"41) SCOT-HEART investigators. CT coronary angiography in patients with suspected angina due to coronary heart disease (SCOT-HEART): an open-label, parallel-group, multicentre trial. Lancet, 2015; 385: 2383-2391","DOI":"10.1016\/S0140-6736(15)60291-4"},{"key":"42","doi-asserted-by":"crossref","unstructured":"42) Patel MR, Peterson ED, Dai D, Brennan JM, Redberg RF, Anderson HV, Brindis RG, Douglas PS. Low diagnostic yield of elective coronary angiography. N. Engl. J. Med., 2010; 362: 886-895","DOI":"10.1056\/NEJMoa0907272"},{"key":"43","doi-asserted-by":"crossref","unstructured":"43) Miller JM, Rochitte CE, Dewey M, Arbab-Zadeh A, Niinuma H, Gottlieb I, Paul N, Clouse ME, Shapiro EP, Hoe J, Lardo AC, Bush DE, de Roos A, Cox C, Brinker J, Lima JA. Diagnostic performance of coronary angiography by 64-row CT. N. Engl. J. Med., 2008; 359: 2324-2336","DOI":"10.1056\/NEJMoa0806576"},{"key":"44","doi-asserted-by":"crossref","unstructured":"44) Mowatt G, Cummins E, Waugh N, Walker S, Cook J, Jia X, Hillis GS, Fraser C. Systematic review of the clinical effectiveness and cost-effectiveness of 64-slice or higher computed tomography angiography as an alternative to invasive coronary angiography in the investigation of coronary artery disease. Health Technol Assess, 2008; 12: iii-iv, ix-143","DOI":"10.3310\/hta12170"},{"key":"45","doi-asserted-by":"crossref","unstructured":"45) Al\u2019Aref SJ, Maliakal G, Singh G, van Rosendael AR, Ma X, Xu Z, Alawamlh OAH, Lee B, Pandey M, Achenbach S, Al-Mallah MH, Andreini D, Bax JJ, Berman DS, Budoff MJ, Cademartiri F, Callister TQ, Chang HJ, Chinnaiyan K, Chow BJW, Cury RC, DeLago A, Feuchtner G, Hadamitzky M, Hausleiter J, Kaufmann PA, Kim YJ, Leipsic JA, Maffei E, Marques H, Gon\u00e7alves PA, Pontone G, Raff GL, Rubinshtein R, Villines TC, Gransar H, Lu Y, Jones EC, Pe\u00f1a JM, Lin FY, Min JK, Shaw LJ. Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry. Eur. Heart J., 2020; 41: 359-367","DOI":"10.1093\/eurheartj\/ehz565"}],"container-title":["Journal of Atherosclerosis and Thrombosis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/jat\/29\/5\/29_62059\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T01:28:53Z","timestamp":1715218133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/jat\/29\/5\/29_62059\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,1]]},"references-count":45,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.5551\/jat.62059","relation":{},"ISSN":["1340-3478","1880-3873"],"issn-type":[{"value":"1340-3478","type":"print"},{"value":"1880-3873","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,1]]},"article-number":"62059"}}