Deep learning classification of early normal-tension glaucoma and glaucoma suspect eyes using Bruch's membrane opening-based disc photography

PurposeWe aimed to investigate the performance of a deep learning model to discriminate early normal-tension glaucoma (NTG) from glaucoma suspect (GS) eyes using Bruch's membrane opening (BMO)-based optic disc photography. Methods501 subjects in total were included in this cross-sectional study, including 255 GS eyes and 246 eyes of early NTG patients. BMO-based optic disc photography (BMO overview) was obtained from spectral-domain optical coherence tomography (OCT). The convolutional neural networks (CNN) model built from scratch was used to classify between early NTG and GS. For diagnostic performances of the model, the accuracy and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) were evaluated in the test set. ResultsThe baseline demographics were age, 48.01 +/- 13.03 years in GS, 54.48 +/- 11.28 years in NTG (p = 0.000); mean deviation, -0.73 +/- 2.10 dB in GS, -2.80 +/- 2.40 dB in NTG (p = 0.000); and intraocular pressure, 14.92 +/- 2.62 mmHg in GS, 14.79 +/- 2.61 mmHg in NTG (p = 0.624). Our CNN model showed the mean AUC of 0.94 (0.83-1.00) and the mean accuracy of 0.91 (0.82-0.98) with 10-fold cross validation for discriminating between early NTG and GS. ConclusionThe performance of the CNN model using BMO-based optic disc photography was considerably good in classifying early NTG from GS. This new disc photography of BMO overview can aid in the diagnosis of early glaucoma.

Automated summary

This study aimed to use a deep learning model based on BMO optic disc photography to discriminate early NTG from GS. The CNN model showed high accuracy and AUC, indicating its potential for diagnosing early glaucoma.