Ophthalmologic Symptoms in a Patient With Autosomal Dominant Multicentric Carpotarsal Osteolysis.

PURPOSE: The purpose of this case report was to provide a detailed description of the ocular manifestations, in a patient with multicentric carpotarsal osteolysis (MCTO), with particular emphasis on bilateral corneal opacities. METHODS: A 43-year-old woman with a history of MCTO was followed with visual acuity assessment and slit-lamp examination at the Department of Ophthalmology in the University Hospitals of Leuven. RESULTS: The patient was found to have bilateral subepithelial haze, along with anterior stromal corneal opacities, and small central lens opacities upon examination. There was a slight corneal thickening. A progression of the corneal opacities was observed, without a further drop in visual acuity. CONCLUSIONS: This case report shows a rare association between MCTO and corneal opacities in adulthood. Interdisciplinary care involving an ophthalmologist is beneficiary for patients with MCTO.

LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images.

Objective.This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes as windows to cardiovascular health.Approach.We utilized active learning to create a new DFI dataset with 240 crowd-sourced manual A/V segmentations performed by 15 medical students and reviewed by an ophthalmologist. We then developed LUNet, a novel deep learning architecture optimized for high-resolution A/V segmentation. The LUNet model features a double dilated convolutional block to widen the receptive field and reduce parameter count, alongside a high-resolution tail to refine segmentation details. A custom loss function was designed to prioritize the continuity of blood vessel segmentation.Main Results.LUNet significantly outperformed three benchmark A/V segmentation algorithms both on a local test set and on four external test sets that simulated variations in ethnicity, comorbidities and annotators.Significance.The release of the new datasets and the LUNet model (www.aimlab-technion.com/lirot-ai) provides a valuable resource for the advancement of retinal microvasculature analysis. The improvements in A/V segmentation accuracy highlight LUNet's potential as a robust tool for diagnosing and understanding cardiovascular diseases through retinal imaging.