Výsledky vyhledávání - "Keith Ameyaw"

Akademický článek

Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence

Autor: Shuo Wang, Daksh Chauhan, Hena Patel, Alborz amir-Khalili, Isabel Ferreira da Silva, Alireza Sojoudi, Silke Friedrich, Amita Singh, Luis Landeras, Tamari Miller, Keith Ameyaw, Akhil Narang, Keigo Kawaji, Qiang Tang, Victor Mor-Avi, Amit R. Patel

Publikováno v: Journal of Cardiovascular Magnetic Resonance, Vol 24, Iss 1, Pp 1-12 (2022)

Abstract Background Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. How

Externí odkaz: https://doaj.org/article/e5f9830461cb4bab9acd292e23240c2d

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Relation of Myocardial Perfusion Reserve and Left Ventricular Ejection Fraction in Ischemic and Nonischemic Cardiomyopathy

Autor: Shuo Wang, Hena Patel, Tamari Miller, Keith Ameyaw, Patrick Miller, Akhil Narang, Keigo Kawaji, Amita Singh, Luis Landeras, Xing-Peng Liu, Victor Mor-Avi, Amit R. Patel

Publikováno v: Am J Cardiol

Quantification of myocardial perfusion reserve (MPR) using vasodilator stress cardiac magnetic resonance is increasingly used to detect coronary artery disease. However, MPR can also be altered because of changes in microvascular function. We aimed t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2960daf8f0651bee445d11a4ec83022c
https://doi.org/10.1016/j.amjcard.2022.02.022

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AI Based CMR Assessment of Biventricular Function

Autor: Amit R. Patel, Roberto M. Lang, Keigo Kawaji, Akhil Narang, Xing-Peng Liu, Tamari Miller, Keith Ameyaw, Shuo Wang, Simran Anand, Stephanie A. Besser, Daksh Chauhan, Hena Patel, Emeka Anyanwu, Victor Mor-Avi

Publikováno v: JACC: Cardiovascular Imaging. 15:413-427

Objectives The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f05ba98d654bb08d39760983d925cbab
https://doi.org/10.1016/j.jcmg.2021.08.011

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Live Imaging of Cutaneous Wound Healing in Zebrafish

Autor: Leah J. Greenspan, Keith Ameyaw, Daniel Castranova, Caleb A. Mertus, Brant M. Weinstein

Cutaneous wounds are common afflictions that follow a stereotypical healing process involving hemostasis, inflammation, proliferation, and remodeling phases. In the elderly or those suffering from vascular or metabolic diseases, poor healing followin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e2dedc9ef2fb7a673b50380f9c3623cb
https://doi.org/10.1101/2022.11.07.515499

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Akademický článek

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Abstract 16189: Cardiovascular Risk Prediction Using Fully Automated Artificial Intelligence Algorithms for the Assessment of Right Ventricular Function From Cardiac Magnetic Resonance Images

Autor: Tamari Miller, Victor Mor-Avi, Keigo Kawaji, Akhil Narang, Patel R Amit, Qiang Tang, Daksh Chauhan, Keith Ameyaw, Shuo Wang, Hena Patel, Stephanie A. Besser

Publikováno v: Circulation. 142

Background: It is unclear whether artificial intelligence (AI) can provide automatic solutions to measure right ventricular ejection fraction (RVEF), due to the complex RV geometry. Although several deep learning (DL) algorithms are available to quan

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::079089fb432ec9086eae8d5abb8fdc84
https://doi.org/10.1161/circ.142.suppl_3.16189

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VALIDATION OF FULLY AUTOMATED DEEP LEARNING BASED ASSESSMENT OF LEFT VENTRICULAR EJECTION FRACTION FROM CARDIAC MAGNETIC RESONANCE IMAGES

Autor: Hena Patel, Akhil Narang, Keigo Kawaji, Amit R. Patel, Victor Mor-Avi, Keith Ameyaw, Shuo Wang, Miller Tamari

Publikováno v: Journal of the American College of Cardiology. 75:1685

New deep learning (DL) algorithms for automated quantification of left ventricular (LV) ejection fraction (EF) from cardiac magnetic resonance (CMR) images need to be validated prior to clinical use. We hypothesized that a novel, fully automated DL a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::993c353ca23eb2af1bbd09c564e0056d
https://doi.org/10.1016/s0735-1097(20)32312-3

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