A method for detecting glaucoma based only on optical coherence tomography (OCT) is of
potential value for routine clinical decisions, for inclusion criteria for research studies and …

Purpose To evaluate the performance of machine learning (ML) in detecting glaucoma using
fundus and retinal optical coherence tomography (OCT) images. Design Meta-analysis …

Recent studies suggest that deep learning systems can now achieve performance on par
with medical experts in diagnosis of disease. A prime example is in the field of …

Recent advancements in artificial intelligence (AI) herald transformative potentials for
reshaping glaucoma clinical management, improving screening efficacy, sharpening …

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various
complex problems related to many areas of healthcare including ophthalmology. AI …

Artificial Intelligence is a rapidly expanding field within computer science that encompasses
the emulation of human intelligence by machines. Machine learning and deep learning–two …

Purpose: The purpose of this study was to improve the diagnostic ability of the optical
coherence tomography (OCT) retinal nerve fiber layer (RNFL) probability (p-) map by …

Purpose To determine whether a deep learning approach using generative adversarial
networks (GANs) is beneficial for the classification of retinal conditions with Optical …

Glaucoma is a leading cause of progressive blindness and visual impairment worldwide.
Microstructural evidence of glaucomatous damage to the optic nerve head and associated …

A leading cause of irreversible vision loss, glaucoma needs early detection for effective
management. Intraocular Pressure (IOP) is a significant risk factor for glaucoma …