Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images.

Idan Bressler¹, Rachelle Aviv¹, Danny Margalit¹, Gal Yaakov Cohen^2,3, Tsontcho Ianchulev^1,4, Shravan V Savant^5,6, David J Ramsey^5,6, Zack Dvey-Aharon¹

¹AEYE Health, Inc., New York, New York.

²The Goldschleger Eye Institute, Sheba Medical Center, Tel Hashomer, Israel.

³Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel.

⁴New York Eye and Ear, Mount Sinai Hospital, New York.

⁵Department of Ophthalmology, Lahey Hospital & Medical Center, Peabody, Massachusetts.

⁶Department of Ophthalmology, Tufts University School of Medicine, Boston, Massachusetts.

PubMed PMC DOI PDF

Abstract

OBJECTIVE: To develop and validate a deep learning model for diabetic macular edema (DME) detection using color fundus imaging, which is applicable in a diverse, multidevice clinical setting. DESIGN: Evaluation of diagnostic test or technology. SUBJECTS: A deep learning model was trained for DME detection using the EyePACS dataset, consisting of 32 049 images from 15 892 patients. The average ag…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.