Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Kristina Dolan"'
Autor:
Ashley N. Beecy, MD, Manasa Gummalla, BA, Evan Sholle, MS, Zhuoran Xu, MSc, Yiye Zhang, MSc, PhD, Kelly Michalak, BA, Kristina Dolan, BA, Yasin Hussain, MD, Benjamin C. Lee, PhD, Yongkang Zhang, PhD, Parag Goyal, MSc, MD, Thomas R. Campion, Jr., PhD, Leslee J. Shaw, PhD, Lohendran Baskaran, MBBS, Subhi J. Al’Aref, MD
Publikováno v:
Cardiovascular Digital Health Journal, Vol 1, Iss 2, Pp 71-79 (2020)
Background: Existing risk assessment tools for heart failure (HF) outcomes use structured databases with static, single-timepoint clinical data and have limited accuracy. Objective: The purpose of this study was to develop a comprehensive approach fo
Externí odkaz:
https://doaj.org/article/ba18048440d746b598b2dae86aead55a
Autor:
Gurpreet Singh, Yasin Hussain, Zhuoran Xu, Evan Sholle, Kelly Michalak, Kristina Dolan, Benjamin C Lee, Alexander R van Rosendael, Zahra Fatima, Jessica M Peña, Peter W F Wilson, Antonio M Gotto, Leslee J Shaw, Lohendran Baskaran, Subhi J Al'Aref
Publikováno v:
PLoS ONE, Vol 15, Iss 9, p e0239934 (2020)
BackgroundLow-density lipoprotein cholesterol (LDL-C) is a target for cardiovascular prevention. Contemporary equations for LDL-C estimation have limited accuracy in certain scenarios (high triglycerides [TG], very low LDL-C).ObjectivesWe derived a n
Externí odkaz:
https://doaj.org/article/bdb9302469f54393b7d2c95f6e14425d
Autor:
Lohendran Baskaran, Zhuoran Xu, Thomas R. Campion, Manasa Gummalla, Parag Goyal, Yasin Hussain, Leslee J. Shaw, Kristina Dolan, Yongkang Zhang, Ashley Beecy, Benjamin C. Lee, Evan Sholle, Yiye Zhang, Subhi J. Al'Aref, Kelly Michalak
Publikováno v:
Cardiovascular Digital Health Journal, Vol 1, Iss 2, Pp 71-79 (2020)
Background Existing risk assessment tools for heart failure (HF) outcomes use structured databases with static, single-timepoint clinical data and have limited accuracy. Objective The purpose of this study was to develop a comprehensive approach for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::710db086e131ef0cef8852cb229fef38
http://www.sciencedirect.com/science/article/pii/S2666693620300104
http://www.sciencedirect.com/science/article/pii/S2666693620300104
Autor:
Umberto Gianni, Paul Knaapen, Gianluca Pontone, Lohendran Baskaran, Wijnand J. Stuijfzand, Benjamin C. Lee, Gabriel Maliakal, Gurpreet Singh, Subhi J. Al'Aref, Zhuoran Xu, Fay Y. Lin, Kelly Michalak, Alexander R. van Rosendael, Hugo Marques, Daniel S. Berman, Mohit Pandey, Hyuk Jae Chang, James K. Min, Leslee J. Shaw, Donghee Han, Kristina Dolan, Inge J. van den Hoogen, Jeroen J. Bax
Publikováno v:
JACC: Cardiovascular Imaging. 13:1163-1171
Objectives This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification. Background Segmentation of cardiac structures from coronary computed tomography angiography (CCTA) images is laborious. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08d9c13e17ed727a2b215cc2fdad1b37
https://doi.org/10.1016/j.jcmg.2019.08.025
https://doi.org/10.1016/j.jcmg.2019.08.025