Artificial intelligence in glaucoma: opportunities, challenges, and future directions

被引：0
作者：
Xiaoqin Huang
Md Rafiqul Islam
Shanjita Akter
Fuad Ahmed
Ehsan Kazami
Hashem Abu Serhan
Alaa Abd-alrazaq
Siamak Yousefi
机构：
[1] University of Tennessee Health Science Center,Department of Ophthalmology
[2] Australian Institute of Higher Education,Business Information Systems
[3] Taylors University,School of Computer Science
[4] Islamic University of Technology (IUT),Department of Computer Science & Engineering
[5] Urmia University of Medical Sciences,Ophthalmology, General Hospital of Mahabad
[6] Hamad Medical Corporations,Department of Ophthalmology
[7] Weill Cornell Medicine-Qatar,AI Center for Precision Health
[8] University of Tennessee Health Science Center,Department of Genetics, Genomics, and Informatics
来源：
BioMedical Engineering OnLine | / 22卷
关键词：
Artificial intelligence; Glaucoma; Machine learning; Deep learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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