Automated Assessment of OCT Angiography Image Quality Using the Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights Data Set.

Jimmy S Chen^1,2,3,4, Lauren E Wedekind^4,5,6, Akshara Legala^4,5, Kevin Tenerelli^4,5,6, Jesse Most^4,5, Naren Ramesh^4,5, Lina Vo^4,5, Jooyoung Chang^1,2, Kyle Marra⁴, Xinyi Ding^1,2, Wei-Chun Lin⁷, Michelle Hribar⁸, Deeba Husain^1,2, Leo A Kim^1,2, Mengyu Wang³, Nimesh A Patel^1,2, John B Miller^1,2, Sally L Baxter⁴

¹Harvard Retinal Imaging Lab, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.

²Retina Service, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.

³Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.

⁴Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California San Diego, La Jolla, California.

⁵University of California San Diego School of Medicine, La Jolla, California.

⁶University of California San Diego Medical Scientist Training Program, University of California San Diego, La Jolla, California.

⁷Department of Ophthalmology, Casey Eye Institute, Oregon Health & Sciences University, Portland, Oregon.

⁸Department of Ophthalmology and Visual Sciences, University of Illinois College of Medicine, Chicago, Illinois.

PubMed PMC DOI PDF

Abstract

OBJECTIVE: OCT angiography (OCTA) images present challenges for clinical and research use due to variability and noise. The aim was to develop artificial intelligence models for OCTA image quality evaluation using the Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) data set. DESIGN: Cross-sectional study. SUBJECTS: Six thousand two hundred sixty-nine OCTA 6 × 6…

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