| Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis M Motwani, D Dey, DS Berman, G Germano, S Achenbach, MH Al-Mallah, ... European heart journal 38 (7), 500-507, 2017 | 730 | 2017 |
| Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review D Dey, PJ Slomka, P Leeson, D Comaniciu, S Shrestha, PP Sengupta, ... Journal of the American College of Cardiology 73 (11), 1317-1335, 2019 | 564 | 2019 |
| Low-attenuation noncalcified plaque on coronary computed tomography angiography predicts myocardial infarction: results from the multicenter SCOT-HEART trial (Scottish Computed … MC Williams, J Kwiecinski, M Doris, P McElhinney, MS D’Souza, S Cadet, ... Circulation 141 (18), 1452-1462, 2020 | 509 | 2020 |
| Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study J Betancur, F Commandeur, M Motlagh, T Sharir, AJ Einstein, S Bokhari, ... JACC: Cardiovascular Imaging 11 (11), 1654-1663, 2018 | 340 | 2018 |
| Pericardial fat burden on ECG-gated noncontrast CT in asymptomatic patients who subsequently experience adverse cardiovascular events VY Cheng, D Dey, B Tamarappoo, R Nakazato, H Gransar, ... JACC: Cardiovascular Imaging 3 (4), 352-360, 2010 | 307 | 2010 |
| Coronary plaque quantification and fractional flow reserve by coronary computed tomography angiography identify ischaemia-causing lesions S Gaur, KA Øvrehus, D Dey, J Leipsic, HE Bøtker, JM Jensen, J Narula, ... European heart journal 37 (15), 1220-1227, 2016 | 299 | 2016 |
| Increased volume of epicardial fat is an independent risk factor for accelerated progression of sub-clinical coronary atherosclerosis A Yerramasu, D Dey, S Venuraju, DV Anand, S Atwal, R Corder, ... Atherosclerosis 220 (1), 223-230, 2012 | 296 | 2012 |
| Pericoronary adipose tissue computed tomography attenuation and high-risk plaque characteristics in acute coronary syndrome compared with stable coronary artery disease M Goeller, S Achenbach, S Cadet, AC Kwan, F Commandeur, PJ Slomka, ... JAMA cardiology 3 (9), 858-863, 2018 | 250 | 2018 |
| Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US D Dey, T Schepis, M Marwan, PJ Slomka, DS Berman, S Achenbach Radiology 257 (2), 516-522, 2010 | 234 | 2010 |
| Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning J Betancur, Y Otaki, M Motwani, MB Fish, M Lemley, D Dey, H Gransar, ... JACC: Cardiovascular Imaging 11 (7), 1000-1009, 2018 | 217 | 2018 |
| Epicardial adipose tissue density and volume are related to subclinical atherosclerosis, inflammation and major adverse cardiac events in asymptomatic subjects M Goeller, S Achenbach, M Marwan, MK Doris, S Cadet, F Commandeur, ... Journal of cardiovascular computed tomography 12 (1), 67-73, 2018 | 205 | 2018 |
| Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT F Commandeur, M Goeller, J Betancur, S Cadet, M Doris, X Chen, ... IEEE transactions on medical imaging 37 (8), 1835-1846, 2018 | 197 | 2018 |
| Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and metabolic syndrome D Dey, ND Wong, B Tamarappoo, R Nakazato, H Gransar, VY Cheng, ... Atherosclerosis 209 (1), 136-141, 2010 | 187 | 2010 |
| Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study D Dey, S Gaur, KA Ovrehus, PJ Slomka, J Betancur, M Goeller, MM Hell, ... European radiology 28, 2655-2664, 2018 | 183 | 2018 |
| Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography M Goeller, BK Tamarappoo, AC Kwan, S Cadet, F Commandeur, ... European Heart Journal-Cardiovascular Imaging 20 (6), 636-643, 2019 | 182 | 2019 |
| Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT B Tamarappoo, D Dey, H Shmilovich, R Nakazato, H Gransar, VY Cheng, ... JACC: Cardiovascular Imaging 3 (11), 1104-1112, 2010 | 172 | 2010 |
| Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study A Lin, N Manral, P McElhinney, A Killekar, H Matsumoto, J Kwiecinski, ... The Lancet Digital Health 4 (4), e256-e265, 2022 | 155 | 2022 |
| Society of Cardiovascular Computed Tomography/North American Society of Cardiovascular Imaging–expert consensus document on coronary CT imaging of atherosclerotic plaque LJ Shaw, R Blankstein, JJ Bax, M Ferencik, MS Bittencourt, JK Min, ... Journal of Cardiovascular Computed Tomography 15 (2), 93-109, 2021 | 154 | 2021 |
| Peri-Coronary Adipose Tissue Density Is Associated With 18F-Sodium Fluoride Coronary Uptake in Stable Patients With High-Risk Plaques J Kwiecinski, D Dey, S Cadet, SE Lee, Y Otaki, PT Huynh, MK Doris, ... JACC: Cardiovascular Imaging 12 (10), 2000-2010, 2019 | 152 | 2019 |
| Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population R Arsanjani, Y Xu, D Dey, V Vahistha, A Shalev, R Nakanishi, S Hayes, ... Journal of Nuclear Cardiology 20 (4), 553-562, 2013 | 144 | 2013 |
