Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the
field of ocular imaging over the last few years. Specifically, DL has been utilised to detect …

Purpose To recognize and address various sources of bias essential for algorithmic fairness
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …

Purpose To discuss the worldwide applications and potential impact of artificial intelligence
(AI) for the diagnosis, management and analysis of treatment outcomes of common retinal …

Importance Retinopathy of prematurity (ROP) is a leading cause of blindness in children,
with significant disparities in outcomes between high-income and low-income countries, due …

Data-driven machine learning, as a promising approach, possesses the capability to build
high-quality, exact, and robust models from ophthalmic medical data. Ophthalmic medical …

This paper presents a federated learning (FL) approach to train deep learning models for
classifying age-related macular degeneration (AMD) using optical coherence tomography …

Objective To compare the performance of deep learning classifiers for the diagnosis of plus
disease in retinopathy of prematurity (ROP) trained using 2 methods for develo** models …

Background Deep learning (DL) is promising to detect glaucoma. However, patients' privacy
and data security are major concerns when pooling all data for model development. We …

A common consequence of diabetes mellitus called diabetic retinopathy (DR) results in
lesions on the retina that impair vision. It can cause blindness if not detected in time …

Purpose Epidemiological changes in retinopathy of prematurity (ROP) depend on neonatal
care, neonatal mortality, and the ability to carefully titrate and monitor oxygen. We evaluate …