Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

ABSTRACT:

Purpose

To compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models.

Methods

Two fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study and Matsue Red Cross Hospital were used to independently develop deep learning algorithms for detection of glaucoma at the University of California, San Diego, and the University of Tokyo. We compared three versions of the University of California, San Diego, and University of Tokyo models: original (no retraining), sequential (retraining only on new data), and combined (training on combined data). Independent datasets were used to test the algorith

SUBMITTER: Christopher M 

PROVIDER: S-EPMC7396194 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature