Can the Future be Bright with Advances in Diabetic Eye Care?

The prevalence of diabetic retinopathy is steadily increasing as the population of patients with diabetes grows. In the past decade, the development of anti-VEGF agents has dramatically changed the treatment landscape for diabetic retinopathy and diabetic macular edema (DME). Newer agents in development aim to reduce the treatment burden of diabetic retinopathy.

Efficient labeling of retinal fundus photographs using deep active learning.

Purpose: To compare the performance of four deep active learning (DAL) approaches to optimize label efficiency for training diabetic retinopathy (DR) classification deep learning models.

Approach: 88,702 color retinal fundus photographs from 44,351 patients with DR grades from the publicly available EyePACS dataset were used. Four DAL approaches [entropy sampling (ES), Bayesian active learning by disagreement (BALD), core set, and adversarial active learning (ADV)] were compared to conventional naive random sampling.

A SYSTEMATIC REVIEW OF DEEP LEARNING APPLICATIONS FOR OPTICAL COHERENCE TOMOGRAPHY IN AGE-RELATED MACULAR DEGENERATION.

Purpose: To survey the current literature regarding applications of deep learning to optical coherence tomography in age-related macular degeneration (AMD).

Methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review was conducted from January 1, 2000, to May 9, 2021, using PubMed and EMBASE databases. Original research investigations that applied deep learning to optical coherence tomography in patients with AMD or features of AMD (choroidal neovascularization, geographic atrophy, and drusen) were included.