The impact of Vitreo-Macular interface abnormalities on the response to Anti-VEGF therapy for centre involving diabetic macular oedema.

Background: The influence of Vitreomacular Interface Abnormalities (VMIA) such as Epiretinal Membrane (ERM) and/or vitreomacular traction (VMT) on the response of patients with Centre Involving Diabetic Macular Edema (CIDME) to standard of care Anti-VEGF medications is under-researched. The aims of this study were: 1) To determine the incidence of VMIA at baseline and 12 months amongst treatment naive patients commencing anti-VEGF treatment 2) To compare the response to Anti-VEGF medications at 3 monthly intervals for 12 months in a large cohort of patients with and without VMIA on their baseline OCT scan. Response was determined in terms of: number of injections, central macular thickness and visual acuity.

Evaluating Text Reading Speed in VR Scenes and 3D Particle Visualizations.

This work reports how text size and other rendering conditions affect reading speeds in a virtual reality environment and a scientific data analysis application. Displaying text legibly yet space-efficiently is a challenging problem in immersive displays. Effective text displays that enable users to read at their maximum speed must consider the variety of virtual reality (VR) display hardware and possible visual exploration tasks.

Improved Outcomes with Heavy Silicone Oil in Complex Primary Retinal Detachment: A Large Multicenter Matched Cohort Study.

Purpose: To establish whether Densiron 68, a heavier-than-water endotamponade agent, is an effective alternative to conventional light silicone oil in primary rhegmatogenous retinal detachment (RD) surgery for eyes with inferior breaks in the detached retina and severe proliferative vitreoretinopathy (PVR).

Design: Cohort study of routinely collected data from the European Society of Retina Specialists and British and Eire Association of Vitreoretinal Surgeons vitreoretinal database between 2015 and 2022.

Participants: All consecutive eyes that underwent primary rhegmatogenous RD surgery using Densiron 68 or light silicone oil as an internal tamponade agent.

Memory-efficient semantic segmentation of large microscopy images using graph-based neural networks.

We present a graph neural network (GNN)-based framework applied to large-scale microscopy image segmentation tasks. While deep learning models, like convolutional neural networks (CNNs), have become common for automating image segmentation tasks, they are limited by the image size that can fit in the memory of computational hardware. In a GNN framework, large-scale images are converted into graphs using superpixels (regions of pixels with similar color/intensity values), allowing us to input information from the entire image into the model.