A generalised computer vision model for improved glaucoma screening using fundus images

Author:

Chaurasia Abadh K1,Liu Guei-Sheung1,Greatbatch Connor J1,Gharahkhani Puya2,Craig Jamie E3,Mackey David A4,MacGregor Stuart2,Hewitt Alex W1

Affiliation:

1. University of Tasmania

2. QIMR Berghofer Medical Research Institute

3. Flinders University

4. Lions Eye Institute, University of Western Australia

Abstract

Abstract Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. Herein, we sought to develop and validate a generalised deep-learning-based algorithm for screening glaucoma using fundus images. We collected glaucomatous data from 20 publicly accessible databases worldwide and selected the best-performing model from 20 pre-trained models. The top-performing model was further trained for classifying healthy and glaucomatous fundus images using Fastai and PyTorch libraries. Gradient-weighted class activation mapping was used to visualise significant areas of fundus images for model decision-making. The best-performing model was validated on 1,364 glaucomatous discs and 2,047 healthy discs. Validation performance metrics indicate robust discriminative ability, with an Area Under the Receiver Operating Characteristic (AUROC) of 0.9920 (95% CI: 0.9920 to 0.9921) for glaucoma and 0.9920 (95% CI: 0.9920 to 0.9921) for healthy class. The model performed well on an external validation (unseen) set of the Drishti-GS dataset, with an AUROC of 0.8751 and an accuracy of 0.8713. Although the model's accuracy slightly decreased when evaluated on unseen data, this study highlighted the potential of computer vision to assist in glaucoma screening.

Publisher

Research Square Platform LLC

Reference62 articles.

1. Prevalence of primary open-angle glaucoma in the last 20 years: a meta-analysis and systematic review;Zhang N;Sci. Rep.,2021

2. The Structure and Function Relationship in Glaucoma: Implications for Detection of Progression and Measurement of Rates of Change;Medeiros FA;Invest. Ophthalmol. Vis. Sci.,2012

3. Glaucoma in Adults—Screening, Diagnosis, and Management: A Review;Stein JD;JAMA,2021

4. A. P. Population screening for glaucoma in UK: current recommendations and future directions;Hamid S;Eye,2022

5. Lessons Learned From 2 Large Community-based Glaucoma Screening Studies;Kolomeyer NN;J. Glaucoma,2021

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3