Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Robert N. Weinreb, MD"'
Autor:
Vincent Michael Patella, OD, Nevin W. El-Nimri, OD, PhD, John G. Flanagan, PhD, Mary K. Durbin, PhD, Timothy Bossie, OD, Derek Y. Ho, MD, PhD, Mayra Tafreshi, MBA, Michael A. Chaglasian, OD, David Kasanoff, OD, Satoshi Inoue, MSc, Sasan Moghimi, MD, Takashi Nishida, MD, PhD, Murray Fingeret, OD, Robert N. Weinreb, MD
Publikováno v:
Ophthalmology Science, Vol 4, Iss 6, Pp 100583- (2024)
Purpose: To construct a comprehensive reference database (RDB) for a novel binocular automated perimeter. Design: A four-site prospective randomized clinical trial. Subjects and Controls: Three hundred fifty-six healthy subjects without ocular condit
Externí odkaz:
https://doaj.org/article/6aa0dbecd79e4c4b8d30453fcc0341ec
Autor:
Jimmy S. Chen, MD, Akshay J. Reddy, BS, Eman Al-Sharif, MD, Marissa K. Shoji, MD, Fritz Gerald P. Kalaw, MD, Medi Eslani, MD, Paul Z. Lang, MD, Malvika Arya, MD, Zachary A. Koretz, MD, MPH, Kyle A. Bolo, MD, Justin J. Arnett, MD, Aliya C. Roginiel, MD, MPH, Jiun L. Do, MD, PhD, Shira L. Robbins, MD, Andrew S. Camp, MD, Nathan L. Scott, MD, Jolene C. Rudell, MD, PhD, Robert N. Weinreb, MD, Sally L. Baxter, MD, MSc, David B. Granet, MD, MHCM
Publikováno v:
Ophthalmology Science, Vol 5, Iss 1, Pp 100600- (2025)
Objective: Large language models such as ChatGPT have demonstrated significant potential in question-answering within ophthalmology, but there is a paucity of literature evaluating its ability to generate clinical assessments and discussions. The obj
Externí odkaz:
https://doaj.org/article/8e2b4fa759ae4e9fb7858fcbdf8a25ff
Autor:
Rui Fan, PhD, Kamran Alipour, PhD, Christopher Bowd, PhD, Mark Christopher, PhD, Nicole Brye, James A. Proudfoot, MS, Michael H. Goldbaum, MD, Akram Belghith, PhD, Christopher A. Girkin, MD, Massimo A. Fazio, PhD, Jeffrey M. Liebmann, MD, Robert N. Weinreb, MD, Michael Pazzani, PhD, David Kriegman, PhD, Linda M. Zangwill, PhD
Publikováno v:
Ophthalmology Science, Vol 3, Iss 1, Pp 100233- (2023)
Purpose: To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS)
Externí odkaz:
https://doaj.org/article/136c451302b347619841acec91a433a5
Autor:
Mohammad Zhalechian, MS, Mark P. Van Oyen, PhD, Mariel S. Lavieri, PhD, Carlos Gustavo De Moraes, MD, PhD, Christopher A. Girkin, MD, Massimo A. Fazio, PhD, Robert N. Weinreb, MD, Christopher Bowd, PhD, Jeffrey M. Liebmann, MD, Linda M. Zangwill, PhD, Christopher A. Andrews, PhD, Joshua D. Stein, MD, MS
Publikováno v:
Ophthalmology Science, Vol 2, Iss 1, Pp 100097- (2022)
Purpose: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indices on perimetry can be enhanced by adding global retinal nerve fiber layer (RNFL) data and whether m
Externí odkaz:
https://doaj.org/article/14e525165773494f8d8623230cca9c3d