Advanced Visualization Engineering for Vision Disorders: A Clinically Focused Guide to Current Technology and Future Applications.

Head-mounted visualization technology, often in the form of virtual, augmented, and mixed reality (VAMR), has revolutionized how visual disorders may be approached clinically. In this manuscript, we review the available literature on VAMR for visual disorders and provide a clinically oriented guide to how VAMR technology has been deployed for visual impairments. The chief areas of clinical investigation with VAMR are divided include (1) vision assessment, (2) vision simulation, and (3) vision rehabilitation. We discuss in-depth the current literature of these areas in VAMR and upcoming/future applications to combat the detrimental impact of visual impairment worldwide.

A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs.

Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on Optical coherence tomography (OCT) technology, called OCT angiography (OCTA), capable of visualizing retina vasculature. However, due to its cost and limited view, OCTA technology is not widely used. Retinal fundus photography is a safe imaging technique used for capturing the overall structure of the retina. In order to visualize retinal vasculature without the need for FA and in a cost-effective, non-invasive, and accurate manner, we propose a deep learning conditional generative adversarial network (GAN) capable of producing FA images from fundus photographs. The proposed GAN produces anatomically accurate angiograms, with similar fidelity to FA images, and significantly outperforms two other state-of-the-art generative algorithms ([Formula: see text] and [Formula: see text]). Furthermore, evaluations by experts shows that our proposed model produces such high quality FA images that are indistinguishable from real angiograms. Our model as the first application of artificial intelligence and deep learning to medical image translation, by employing a theoretical framework capable of establishing a shared feature-space between two domains (i.e. funduscopy and fluorescein angiography) provides an unrivaled way for the translation of images from one domain to the other.