Machine Learning OCT Predictors of Progression from Intermediate Age-Related Macular Degeneration to Geographic Atrophy and Vision Loss

Machine Learning OCT Predictors of Progression from Intermediate Age-Related Macular Degeneration to Geographic Atrophy and Vision Loss

Purpose: To describe spectral-domain OCT (SD-OCT) features, age, gender, and systemic variables that may be used in machine/deep learning studies to identify high-risk patient subpopulations with high risk of progression to geographic atrophy (GA) and visual acuity (VA) loss in the short term. Desig...

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Bibliographic Details
Main Authors: Eleonora M. Lad, MD, PhD, Karim Sleiman, MD, David L. Banks, PhD, Sanjay Hariharan, MS, Traci Clemons, PhD, Rolf Herrmann, PhD, Daniyar Dauletbekov, MD, PhD, Andrea Giani, MD, Victor Chong, MD, MBA, Emily Y. Chew, MD, Cynthia A. Toth, MD, Cynthia A. Toth, Wai Wong, Thomas Huang, G. Baker Hubbard, Sunil Srivastava, Michelle McCall, Katrina Winter, Neeru Sarin, Katherine Hall, Patti McCollum, Linda Curtis, Stefanie Schuman, Stephanie J. Chiu, Sina Farsiu, Vincent Tai, Traci Clemons, Emily Chew
Format: Article
Language:English
Published: Elsevier 2022-06-01
Series:Ophthalmology Science
Subjects:
Classification trees
Geographic atrophy
Machine learning
Prediction
Random forest
Online Access:http://www.sciencedirect.com/science/article/pii/S2666914522000495

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http://www.sciencedirect.com/science/article/pii/S2666914522000495

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