Grant Duffy
17 Papers
Grant Duffy is an academic researcher. The author has contributed to research in topics: Internal medicine & Computer science. The author has an hindex of 2, co-authored 6 publications.
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Papers
Blinded, randomized trial of sonographer versus AI cardiac function assessment
Bryan He,Alan C. Kwan,Jae H Cho,Neal Yuan,Charles Pollick,Takahiro Shiota,Joseph E. Ebinger,Natalie A. Bello,Janet Wei,Kiranbir Josan,Grant Duffy,Melvin Jujjavarapu,Robert J. Siegel,S. Cheng,James Zou,David W. Ouyang +15 more
TL;DR: In this article , the authors designed a randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF).
Confounders mediate AI prediction of demographics in medical imaging
Grant Duffy,Shoa L. Clarke,M. Christensen,Bryan He,Neal Yuan,Susan Cheng,David W. Ouyang +6 more
- 22 Dec 2022
TL;DR: In this article , the authors used deep learning to predict age, race, and sex from cardiac ultrasound images using deep learning algorithms and assess the impact of varying confounding variables, including confounding differences between categories.
Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.
Neal Yuan,Grant Duffy,Sanket S. Dhruva,Adam Oesterle,Cara N. Pellegrini,J. Theurer,Marzieh Vali,Paul A Heidenreich,Salomeh Keyhani,David Ouyang +9 more
TL;DR: Whether deep learning models applied to outpatient ECGs in sinus rhythm can predict atrial fibrillation in a large and diverse patient population is determined to determine.
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Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms
Neal Yuan,Alan C. Kwan,Grant Duffy,J. Theurer,Jonathan H. Chen,Koen Nieman,Patrick Botting,Damini Dey,Daniel S. Berman,Susan Cheng,David W. Ouyang +10 more
TL;DR: In this paper , a video-based artificial intelligence convolutional neural network was used to predict coronary artery calcification (CAC) scores from parasternal long-axis views.
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Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study
David Ouyang,J. Theurer,Nathan R Stein,J. W. Hughes,Pierre Elias,Bryan He,Neal Yuan,Grant Duffy,Roopinder K. Sandhu,Joseph E. Ebinger,Brian Claggett,Jonathan H Chen,Michael Nurok,Marco V Perez,Nancy Cook,Sumeet S. Chugh,Christine M. Albert +16 more
TL;DR: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality and can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications.
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