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

PURPOSE

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

DESIGN

Retrospective longitudinal cohort study.

PARTICIPANTS

1,510 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 (CNN) was designed to assess baseline and final DPs to detect glaucoma progression.

MAIN OUTCOME

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

RESULTS

Baseline visual field mean deviation (±SD) was -4.6 (±6.1) dB. 22% of eyes deteriorated based on the clinical review of DPs. The final DL model's AUC (95% CI) 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.

CONCLUSION

Our twin CNN 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.