Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning.

ABSTRACT:

Purpose

Standard automated perimetry is the gold standard to monitor visual field (VF) loss in glaucoma management, but it is prone to intrasubject variability. We trained and validated a customized deep learning (DL) regression model with Xception backbone that estimates pointwise and overall VF sensitivity from unsegmented optical coherence tomography (OCT) scans.

Methods

DL regression models have been trained with four imaging modalities (circumpapillary OCT at 3.5 mm, 4.1 mm, and 4.7 mm diameter) and scanning laser ophthalmoscopy en face images to estimate mean deviation (MD) and 52 threshold values. This retrospective study used data from patients who underwent a complete glaucoma examination, including a reliable Humphrey Field Analyzer (HFA) 24-2 SITA Standard (SS) VF

SUBMITTER: Hemelings R 

PROVIDER: S-EPMC9424967 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature