Detection of Structural Glaucoma Progression with Deep Learning on Serial Optic Disc Photographs

Abstract

Purpose

To design a supervised deep learning (DL) model to detect glaucoma progression with serial optic disc photographs (DPs).

Design

A retrospective longitudinal cohort study.

Participants

One thousand five hundred ten eyes (916 patients) with ≥2 years of follow-up and 2 pairs of DPs per eye were included.

Methods

Longitudinal series of DPs were labeled as having evidence of progression or stable by 2 ophthalmologists, and discrepancies were adjudicated by 2 glaucoma specialists. An automated cropping was applied centered on the optic disc to reduce less relevant information. The dataset was split into training and testing/validation sets with an 80/10/10 ratio. A twin convolutional neural network was designed to assess baseline and final DPs to detect glaucoma progression.

Main Outcome Measures

Area under receiver operating characteristic curves (AUCs) for detection of glaucoma progression; sensitivity and specificity for automated classification compared to clinical classification as ground truth.

Results

Baseline visual field mean deviation (±standard deviation) was –4.0 (±5.6) dB. Twenty-two percent of eyes deteriorated based on the clinical review of DPs. The final DL model’s AUC (95% confidence interval) for detection of glaucoma progression was 0.821 (0.764–0.887) with an overall accuracy for classification of 72% (66%–88%) with a sensitivity and specificity of 87% (60%–95%) and 68% (61%–93%), respectively.

Conclusions

Our twin convolutional neural network model is able to detect glaucoma progression with clinically relevant accuracy. Deep learning is promising as an adjunctive method for clinical decision-making for detection of structural glaucoma progression.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.