A
Aaron Y. Lee
Researcher at University of Washington
Publications - 143
Citations - 5441
Aaron Y. Lee is an academic researcher from University of Washington. The author has contributed to research in topics: Visual acuity & Deep learning. The author has an hindex of 26, co-authored 137 publications receiving 3580 citations. Previous affiliations of Aaron Y. Lee include Veterans Health Administration & University of British Columbia.
Papers
More filters
Journal ArticleDOI
Artificial intelligence and deep learning in ophthalmology
Daniel Shu Wei Ting,Louis R. Pasquale,Lily Peng,John P. Campbell,Aaron Y. Lee,Rajiv Raman,Gavin Tan,Leopold Schmetterer,Pearse A. Keane,Tien Yin Wong +9 more
TL;DR: There are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms.
Journal ArticleDOI
Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC)
Benjamin M. Neale,Jesen Fagerness,Jesen Fagerness,Robyn Reynolds,Lucia Sobrin,Margaret M. Parker,Soumya Raychaudhuri,Soumya Raychaudhuri,Perciliz L. Tan,Edwin C. Oh,Joanna E. Merriam,Eric H. Souied,Paul S. Bernstein,Binxing Li,Jeanne M. Frederick,Kang Zhang,Kang Zhang,Milam A. Brantley,Aaron Y. Lee,Donald J. Zack,Betsy Campochiaro,Peter A. Campochiaro,Stephan Ripke,Stephan Ripke,R. Theodore Smith,Gaetano R. Barile,Nicholas Katsanis,Rando Allikmets,Mark J. Daly,Mark J. Daly,Johanna M. Seddon +30 more
TL;DR: A genome-wide association study of advanced age-related macular degeneration cases and controls using the Affymetrix 6.0 platform implicate different biologic pathways than previously reported and provide new avenues for prevention and treatment of AMD.
Journal ArticleDOI
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration.
TL;DR: Deep learning techniques achieve high accuracy and is effective as a new image classification technique in Optical coherence tomography and have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.
Journal ArticleDOI
Deep-learning based, automated segmentation of macular edema in optical coherence tomography
TL;DR: A convolutional neural network (CNN) is developed that detects intraretinal fluid (IRF) on OCT in a manner indistinguishable from clinicians and can be trained to perform automated segmentations of clinically relevant image features.
Journal ArticleDOI
Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration
Yi Yu,Tushar Bhangale,Jesen Fagerness,Jesen Fagerness,Stephan Ripke,Stephan Ripke,Gudmar Thorleifsson,Perciliz L. Tan,Eric H Souied,Andrea J. Richardson,Joanna E. Merriam,Gabriëlle H.S. Buitendijk,Robyn Reynolds,Soumya Raychaudhuri,Kimberly A Chin,Lucia Sobrin,Evangelos Evangelou,Phil Lee,Phil Lee,Aaron Y. Lee,Nicolas Leveziel,Donald J. Zack,Betsy Campochiaro,Peter A. Campochiaro,R. Theodore Smith,Gaetano R. Barile,Robyn H. Guymer,Ruth E Hogg,Usha Chakravarthy,Luba D Robman,Omar Gustafsson,Haraldur Sigurdsson,Ward Ortmann,Timothy W. Behrens,Kari Stefansson,Kari Stefansson,André G. Uitterlinden,Cornelia M. van Duijn,Johannes R. Vingerling,Caroline C W Klaver,Rando Allikmets,Milam A. Brantley,Paul N. Baird,Nicholas Katsanis,Unnur Thorsteinsdottir,Unnur Thorsteinsdottir,John P. A. Ioannidis,Mark J. Daly,Mark J. Daly,Robert R. Graham,Johanna M. Seddon +50 more
TL;DR: The novel variants identified in this study suggest that angiogenesis (VEGFA) and extracellular collagen matrix (FRK/COL10A1) pathways contribute to the development of advanced AMD.