Measures of disease activity in glaucoma.

Abstract

Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future.

• Authors: Yue Wu, Maja Szymanska, Yubing Hu, M Ihsan Fazal, Nan Jiang, Ali K Yetisen, M Francesca Cordeiro
• Journal: Biosensors & bioelectronics (Biosens Bioelectron) Vol. 196 Pg. 113700 (Jan 15 2022) ISSN: 1873-4235 [Electronic] England
• PMID: 34653715 (Publication Type: Journal Article, Review)
• Copyright: Copyright © 2021 Elsevier B.V. All rights reserved.

Topics

• Artificial Intelligence
• Biosensing Techniques
• Glaucoma (diagnosis)
• Humans
• Quality of Life
• Visual Field Tests