Automated detection of Retinal exudates and drusen in ultra-
Researchers aimed to develop and assess a deep learning system for automated detection of RED using ultra-widefield fundus (UWF) images. 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.

A total of 26,409 UWF images from 14,994 subjects were used to develop and evaluate the deep learning system. The saliency map visualization technique was used to understand which areas in the UWF image had the most influence on our deep learning system when detecting RED.

--The system for RED detection achieved areas under the receiver operating characteristic curve of 0.994, 0.972, and 0.988 in three independent datasets.

--The performance of the system in the ZOC dataset was comparable to that of an experienced retina specialist.

--Regions of RED were highlighted by saliency maps in UWF images.

In conclusion, this method is capable of detecting RED in UWF photos automatically. It may aid in the early detection and management of RED-related fundus illnesses as a screening technique.