Automated detection of retinal exudates and drusen in ultra-widefield fundus images based on deep learning.

ABSTRACT:

Background

Retinal exudates and/or drusen (RED) can be signs of many fundus diseases that can lead to irreversible vision loss. Early detection and treatment of these diseases are critical for improving vision prognosis. However, manual RED screening on a large scale is time-consuming and labour-intensive. Here, we aim to develop and assess a deep learning system for automated detection of RED using ultra-widefield fundus (UWF) images.

Methods

A total of 26,409 UWF images from 14,994 subjects were used to develop and evaluate the deep learning system. The Zhongshan Ophthalmic Center (ZOC) dataset was selected to compare the performance of the system to that of retina specialists in RED detection. The saliency map visualization technique was used to understand which areas in

SUBMITTER: Li Z 

PROVIDER: S-EPMC9307785 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature