Damini Dey
Damini Dey
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center
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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
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
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
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
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
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
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
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
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
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
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
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 (6), 2655-2664, 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
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
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
Impact of family history of coronary artery disease in young individuals (from the CONFIRM registry)
Y Otaki, H Gransar, DS Berman, VY Cheng, D Dey, FY Lin, S Achenbach, ...
The American journal of cardiology 111 (8), 1081-1086, 2013
Automated 3-dimensional registration of stand-alone 18F-FDG whole-body PET with CT
PJ Slomka, D Dey, C Przetak, UE Aladl, RP Baum
Journal of Nuclear Medicine 44 (7), 1156-1167, 2003
Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population
R Arsanjani, D Dey, T Khachatryan, A Shalev, SW Hayes, M Fish, ...
Journal of Nuclear Cardiology 22 (5), 877-884, 2015
Epicardial and thoracic fat-Noninvasive measurement and clinical implications
D Dey, R Nakazato, D Li, DS Berman
Cardiovascular diagnosis and therapy 2 (2), 85, 2012
Automatic fusion of freehand endoscopic brain images to three-dimensional surfaces: creating stereoscopic panoramas
D Dey, DG Gobbi, PJ Slomka, KJM Surry, TM Peters
IEEE Transactions on Medical Imaging 21 (1), 23-30, 2002
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