PURPOSE: To estimate central 10-degree visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thickness (RNFL) measurements in glaucoma with artificial intelligence.
This paper describes a new method of textile pilling prediction, based on multivariate analysis of the spatial layer above the surface. The original idea of the method is the acquisition of 3D fabric image using optical coherence tomography (OCT) wit...
In recent years, software-assisted imaging systems, such as computed tomography, have contributed to the improvement of noninvasive options for the diagnostics of coronary heart disease (CHD). In addition, the possibilities of individual morphologica...
Annals of clinical and translational neurology
Oct 25, 2022
BACKGROUND: The diagnosis of multiple sclerosis (MS) requires demyelinating events that are disseminated in time and space. Peripapillary retinal nerve fiber layer (pRNFL) thickness as measured by optical coherence tomography (OCT) distinguishes eyes...
Clinical & experimental ophthalmology
Oct 17, 2022
BACKGROUND: The aim was to explore the feasibility and safety of performing common surgical steps in epiretinal membrane (ERM) peeling using the Preceyes Surgical System (PSS).
This work aims at determining the ability of a deep learning (DL) algorithm to measure retinal nerve fiber layer (RNFL) thickness from optical coherence tomography (OCT) scans in anterior ischemic optic neuropathy (NAION) and demyelinating optic neur...
PURPOSE: The diagnosis of cataract is mostly clinical and there is a lack of objective and specific tool to detect and grade it automatically. The goal of this study was to develop and validate a deep learning model to detect and localize cataract on...
Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and prov...
INTRODUCTION: Development and validation of a deep learning algorithm to automatically identify and locate epiretinal memberane (ERM) regions in OCT images.
PURPOSE: To create an unsupervised cross-domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic macular OCT images from different manufacturers and camera devices.