Comparison of machine learning approaches for structure–function modeling in glaucoma-Reference-Cited by-同舟云学术

Comparison of machine learning approaches for structure–function modeling in glaucoma

Published:2022-06-21 Issue:1 Volume:1515 Page:237-248
ISSN:0077-8923
Container-title:Annals of the New York Academy of Sciences
language:en
Short-container-title:Annals NY Academy of Science

Author:

Wong Damon¹²³⁴^ORCID,Chua Jacqueline¹³,Bujor Inna⁵,Chong Rachel S.³,Nongpiur Monisha E.³,Vithana Eranga N.³,Husain Rahat³,Aung Tin³⁶,Popa‐Cherecheanu Alina⁵⁷,Schmetterer Leopold¹²³⁴⁸

Affiliation:

1. SERI‐NTU Advanced Ocular Engineering (STANCE) Singapore

2. School of Chemical and Biomedical Engineering Nanyang Technological University Singapore

3. Singapore Eye Research Institute Singapore National Eye Centre Singapore

4. Institute of Molecular and Clinical Ophthalmology Basel Switzerland

5. Carol Davila University of Medicine and Pharmacy Bucharest Romania

6. Yong Loo Lin School of Medicine National University of Singapore Singapore

7. Department of Ophthalmology Emergency University Hospital Bucharest Romania

8. Department of Clinical Pharmacology Medical University of Vienna Vienna Austria

Funder

National Research Foundation Singapore

National Medical Research Council

Agency for Science, Technology and Research

Publisher

Wiley

Subject

History and Philosophy of Science,General Biochemistry, Genetics and Molecular Biology,General Neuroscience

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/nyas.14844

Reference46 articles.

1. Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040

2. OCT for glaucoma diagnosis, screening and detection of glaucoma progression

3. Glaucoma Research Community and FDA Look to the Future, II: NEI/FDA Glaucoma Clinical Trial Design and Endpoints Symposium: Measures of Structural Change and Visual Function

4. Strukturelle Endpunkte für Glaukomstudien

5. Practical recommendations for measuring rates of visual field change in glaucoma

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