Glaucoma diagnosis using multi-feature analysis and a deep learning technique

Abstract

In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.

Applications of Artificial Intelligence and Deep Learning in Glaucoma

“…The authors developed a convolutional neural network (CNN) algorithm for calculating CDR. Since then, algorithms have become increasingly sophisticated and clinically targeted toward the identification of referable glaucoma 14,17–23. For example, Bhuiyan and colleagues developed one of the first proprietary semiautomated software to quantify vertical CDR.…”

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases

“…The cycle generative adversarial network (cycle GAN)—based feature maps show hidden features of superficial ACD that are undetectable by traditional techniques and ophthalmologists and help detect early ACD ( 66 ). Some investigators have analyzed multiple features and introduced new cross-sectional ONH features from OCT images to facilitate the current diagnostic evaluation of glaucoma, demonstrating that selected features and cross-sectional ONH cup areas trained using DL have great potential as preliminary screening tools for glaucoma ( 67 ). These results will help clinicians make more accurate decisions in the future.…”

“…The investigators developed and evaluated the performance of a DL system based on a smartphone app through efficient glaucoma diagnostic workers based on VFs, providing keening to detect visual field changes in glaucoma with smartphones ( 67 ). Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs).…”

Artificial intelligence in glaucoma: posterior segment optical coherence tomography

“…In a 2022 study, Akter et al [ 38 ▪▪ ] developed the first AI method for glaucoma detection which accounts for functional, structural, and risk factor data. Notably, a new region of interest from ONH OCT B-scans, the cup surface area, was calculated and used as a parameter to train three DL models which achieved an AUC of 0.99 when discriminating glaucomatous from healthy eyes.…”