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

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, were included. Disc photos were obtained within 6 months of OCT. Data were randomly selected for training and testing of the DL models. Testing: Development of DL models utilizing either OCT RNFLT maps or DPs to detect glaucoma based on VF-defined functional impairment. Main Outcome Measures: The primary outcome was the area under the curve (AUC) for glaucoma detection, comparing the OCT-based DL model with the DP-based model. The secondary outcome was the AUC across demographic groups. Results: The OCT-based DL model achieved an AUC of 0.90, significantly outperforming the DP-based model (AUC = 0.86, Conclusions: When glaucoma diagnosis was based on functional deficit, the OCT-based DL model offered greater accuracy in detecting glaucoma than the DP-based model, likely due to its use of objective and quantitative RNFLT measurements. This work supports the use of OCT-based DL models for glaucoma detection, while observed demographic disparities underscore the need for equitable datasets to ensure fair DL-driven glaucoma diagnosis across populations. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.