Deep Learning with Disc Photos or OCT Scans in Glaucoma Detection.

ABSTRACT:

Objective

To determine whether a deep learning (DL) model using retinal nerve fiber layer thickness (RNFLT) maps from OCT scans can detect glaucoma, defined by functional visual field (VF) impairment, more accurately than a DL model using disc photos (DPs). A secondary objective was to assess the diagnostic performance of these DL models across demographic groups (race, sex, and ethnicity).

Design

Retrospective cohort study at a tertiary glaucoma center utilizing OCT and DP datasets collected between 2011 and 2022.

Participants

Out of the 16 936 DP and OCT image sets, patients with Cirrus OCT images with a quality score ≥6 of 10 and reliable 24-2 Humphrey VF tests (fixation loss ≤33%, false-negative rate ≤20%, false-positive rate ≤20%), taken within 30 days of OCT, we

SUBMITTER: Katuru A 

PROVIDER: S-EPMC12395170 | biostudies-literature | 2025 Nov-Dec

REPOSITORIES: biostudies-literature