David Ouyang
Cedars-Sinai Medical Center
44 Papers
61 Citations
David Ouyang is an academic researcher from Cedars-Sinai Medical Center. The author has contributed to research in topics: Medicine & Deep learning. The author has an hindex of 15, co-authored 43 publications. Previous affiliations of David Ouyang include Stanford University & University of California, San Francisco.
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Papers
Video-based AI for beat-to-beat assessment of cardiac function.
David Ouyang,Bryan He,Amirata Ghorbani,Neal Yuan,Joseph E. Ebinger,Curtis P. Langlotz,Paul A. Heidenreich,Robert A. Harrington,David Liang,Euan A. Ashley,James Zou +10 more
TL;DR: A video-based deep learning algorithm that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy is presented.
Deep learning interpretation of echocardiograms.
Amirata Ghorbani,David Ouyang,Abubakar Abid,Bryan He,Jonathan H. Chen,Robert A. Harrington,David Liang,Euan A. Ashley,James Zou +8 more
- 24 Jan 2020
TL;DR: Using convolutional neural networks on a large new dataset, deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation.
How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
TL;DR: A comprehensive overview of medical AI devices approved by the US Food and Drug Administration sheds new light on limitations of the evaluation process that can mask vulnerabilities of devices when they are deployed on patients as mentioned in this paper.
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Epilepsy surgery trends in the United States, 1990–2008
TL;DR: Nationwide time trends of resective surgery for the treatment of medically refractory epilepsy before and after Class I evidence demonstrating its efficacy and subsequent practice guidelines recommending early surgical evaluation showed an overall trend of decreasing surgery rates.
Deep Learning Interpretation of Echocardiograms
Amirata Ghorbani,David Ouyang,Abubakar Abid,Bryan He,Jonathan H. Chen,Robert A. Harrington,David Liang,Euan A. Ashley,James Zou +8 more
TL;DR: It is shown that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation.