Assessment of choroidal vessels in healthy eyes using 3-dimensional vascular maps and a semi-automated deep learning approach.

To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach.

Tppp3 is a novel molecule for retinal ganglion cell identification and optic nerve regeneration.

Mammalian central nervous system (CNS) axons cannot spontaneously regenerate after injury, creating an unmet need to identify molecular regulators to promote axon regeneration and reduce the lasting impact of CNS injuries. While tubulin polymerization promoting protein family member 3 (Tppp3) is known to promote axon outgrowth in amphibians, its role in mammalian axon regeneration remains unknown. Here we investigated Tppp3 in retinal ganglion cells (RGCs) neuroprotection and axonal regeneration using an optic nerve crush (ONC) model in the rodent.

Five-Year Outcomes of Lenadogene Nolparvovec Gene Therapy in Leber Hereditary Optic Neuropathy.

Importance: Limited studies have assessed the long-term benefit/risk of gene therapy for Leber hereditary optic neuropathy (LHON).

Objective: To determine the safety and efficacy of lenadogene nolparvovec in patients with LHON due to the MT-ND4 gene variant for up to 5 years after administration.

Design, Setting, And Participants: The RESCUE and REVERSE Long-Term Follow-up Study (RESTORE), conducted from 2018 to 2022, is the 5-year follow-up study of the 2 phase 3 clinical studies RESCUE (Efficacy Study of Lenadogene Nolparvovec for the Treatment of Vision Loss Up to 6 Months From Onset in LHON Due to the MT-ND4 Mutation) and REVERSE (Efficacy Study of Lenadogene Nolparvovec for the Treatment of Vision Loss From 7 Months to 1 Year From Onset in LHON Due to the MT-ND4 Mutation).

Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.

Objectives: Various imaging features on optical coherence tomography (OCT) are crucial for identifying and defining disease progression. Establishing a consensus on these imaging features is essential, particularly for training deep learning models for disease classification. This study aims to analyze the inter-rater reliability in labeling the quality and common imaging signatures of retinal OCT scans.

BreakNet: discontinuity-resilient multi-scale transformer segmentation of retinal layers.

Visible light optical coherence tomography (vis-OCT) is gaining traction for retinal imaging due to its high resolution and functional capabilities. However, the significant absorption of hemoglobin in the visible light range leads to pronounced shadow artifacts from retinal blood vessels, posing challenges for accurate layer segmentation. In this study, we present BreakNet, a multi-scale Transformer-based segmentation model designed to address boundary discontinuities caused by these shadow artifacts.