Topic: Age-related Macular Degeneration

Photoreceptor loss is the primary cause behind vision impairment and blindness in diseases such as retinitis pigmentosa and age-related macular degeneration. However, the death of rods and cones allows retinoids to permeate the inner retina, causing retinal ganglion cells to become spontaneously hyperactive, severely reducing the signal-to-noise ratio, and creating interference in the communication between the surviving retina and the brain. Treatments aimed at blocking or reducing hyperactivity improve vision initiated from surviving photoreceptors and could enhance the signal fidelity genera

Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classi

Age-related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP) are vision disorders caused by loss of rod and cone photoreceptors, but downstream retinal neurons also show physiological and morphological changes, resulting in the emergence of hyperactivity and rhythmic firing in many retinal ganglion cells (RGC). We recently discovered that retinoic acid (RA) is a key signal that triggers hyperactivity and that blockers of RA unmask light responses in RGCs that would otherwise be obscured. Recent work is revealing where in the retina circuit RA initiates functional changes. Moreover, int