| 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 | 494 | 2019 |
| Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation … PP Sengupta, S Shrestha, B Berthon, E Messas, E Donal, GH Tison, ... Cardiovascular Imaging 13 (9), 2017-2035, 2020 | 143 | 2020 |
| Artificial intelligence: practical primer for clinical research in cardiovascular disease N Kagiyama, S Shrestha, PD Farjo, PP Sengupta Journal of the American Heart Association 8 (17), e012788, 2019 | 134 | 2019 |
| Network tomography for understanding phenotypic presentations in aortic stenosis G Casaclang-Verzosa, S Shrestha, MJ Khalil, JS Cho, M Tokodi, S Balla, ... JACC: Cardiovascular Imaging 12 (2), 236-248, 2019 | 76 | 2019 |
| Machine learning assessment of left ventricular diastolic function based on electrocardiographic features N Kagiyama, M Piccirilli, N Yanamala, S Shrestha, PD Farjo, ... Journal of the American College of Cardiology 76 (8), 930-941, 2020 | 73 | 2020 |
| Artificial intelligence in cardiovascular medicine K Seetharam, S Shrestha, PP Sengupta Current treatment options in cardiovascular medicine 21, 1-14, 2019 | 68 | 2019 |
| A machine-learning framework to identify distinct phenotypes of aortic stenosis severity PP Sengupta, S Shrestha, N Kagiyama, Y Hamirani, H Kulkarni, ... Cardiovascular Imaging 14 (9), 1707-1720, 2021 | 54 | 2021 |
| Interpatient similarities in cardiac function: a platform for personalized cardiovascular medicine M Tokodi, S Shrestha, C Bianco, N Kagiyama, G Casaclang-Verzosa, ... Cardiovascular Imaging 13 (5), 1119-1132, 2020 | 43 | 2020 |
| Machine learning for nuclear cardiology: The way forward S Shrestha, PP Sengupta Journal of Nuclear Cardiology 26, 1755-1758, 2019 | 42 | 2019 |
| A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound N Kagiyama, S Shrestha, JS Cho, M Khalil, Y Singh, A Challa, ... EBioMedicine 54, 2020 | 41 | 2020 |
| A network-based “phenomics” approach for discovering patient subtypes from high-throughput cardiac imaging data JS Cho, S Shrestha, N Kagiyama, L Hu, YA Ghaffar, ... JACC: Cardiovascular Imaging 13 (8), 1655-1670, 2020 | 32 | 2020 |
| Machine learning for data-driven discovery: the rise and relevance PP Sengupta, S Shrestha JACC: Cardiovascular Imaging 12 (4), 690-692, 2019 | 25 | 2019 |
| Cardiovascular imaging and intervention through the lens of artificial intelligence K Seetharam, S Shrestha, PP Sengupta Interventional Cardiology: Reviews, Research, Resources 16, 2021 | 21 | 2021 |
| Artificial intelligence in cardiac imaging K Seetharam, S Shrestha, PP Sengupta US Cardiology Review 13 (2), 110-116, 2019 | 15 | 2019 |
| CT assessment of the left atrial appendage post-transcatheter occlusion–a systematic review and meta analysis S Banga, M Osman, PP Sengupta, MM Benjamin, S Shrestha, A Challa, ... Journal of cardiovascular computed tomography 15 (4), 348-355, 2021 | 14 | 2021 |
| The mechanics of machine learning: from a concept to value S Shrestha, PP Sengupta Journal of the American Society of Echocardiography 31 (12), 1285-1287, 2018 | 14 | 2018 |
| Imaging heart failure with artificial intelligence: improving the realism of synthetic wisdom S Shrestha, PP Sengupta Circulation: Cardiovascular Imaging 11 (4), e007723, 2018 | 14 | 2018 |
| Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation … PP Sengupta, S Shrestha, B Berthon, E Messas, E Donal, GH Tison, ... Epub 2020/09/12. https://doi. org/10.1016/j. jcmg. 2020.07. 015 PMID …, 0 | 12 | |
| Clinical inference from cardiovascular imaging: paradigm shift towards machine-based intelligent platform K Seetharam, N Kagiyama, S Shrestha, PP Sengupta Current Treatment Options in Cardiovascular Medicine 22, 1-11, 2020 | 11 | 2020 |
| Usefulness of semisupervised machine-learning-based phenogrouping to improve risk assessment for patients undergoing transcatheter aortic valve implantation YA Ghffar, M Osman, S Shrestha, F Shaukat, N Kagiyama, M Alkhouli, ... The American Journal of Cardiology 136, 122-130, 2020 | 10 | 2020 |
Nobuyuki KagiyamaJuntendo UniversityDirección de correo verificada de juntendo.ac.jp
