An Artificial-Intelligence- and Telemedicine-Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging.

Alauddin Bhuiyan,Arun Govindaiah,R Theodore Smith,Heba A El Gendy

doi:10.1155/2021/6694784

Abstract

Results The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85). Conclusions Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings.

Highlights

Glaucoma is a group of diseases that damage the eye’s optic nerve and result in vision loss and blindness [1]
Glaucoma is characterized by loss of retinal ganglion cells (RGCs), which results in visual field impairment and structural changes to the retinal nerve fiber layer (RNFL) and optic disc [4]
We developed and validated our AIbased glaucoma-suspect screening results based on the quantified vertical cup-to-disc ratio (CDR). is should provide higher accuracy and confidence than selective judgment

Summary

Introduction

Glaucoma is a group of diseases that damage the eye’s optic nerve and result in vision loss and blindness [1]. E social and economic costs of vision loss from glaucoma are extremely high. Detection of these conditions halts a downward spiral in overall health: depression, loss of independence, need for nursing home care, falls, fractures, and death. 1546 disc-centered fundus images were selected, including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images were randomly selected from the Age-Related Eye Disease Study and Singapore Malay Eye Study to develop the system. E binary classifier, with glaucoma suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC. Having demonstrated an accurate and fully automated glaucoma-suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary-care settings

Objectives

Findings

Discussion

Conclusion