Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images-Reference-Cited by-同舟云学术

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Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images

Published:2024-04-03 Issue:4 Volume:13 Page:6
ISSN:2164-2591
Container-title:Translational Vision Science & Technology
language:en
Short-container-title:Trans. Vis. Sci. Tech.

Author:

Yap Boon Peng¹,Kelvin Li Zhenghao²³⁴,Toh En Qi³,Low Kok Yao²⁴,Rani Sumaya Khan²⁴,Goh Eunice Jin Hui²⁴,Hui Vivien Yip Cherng²⁴,Ng Beng Koon¹,Lim Tock Han²³⁴

Affiliation:

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

2. Department of Ophthalmology, Tan Tock Seng Hospital, Singapore, Singapore

3. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

4. National Healthcare Group Eye Institute, Singapore, Singapore

Publisher

Association for Research in Vision and Ophthalmology (ARVO)

Reference32 articles.

1. Number of people with glaucoma worldwide;Quigley;Br J Ophthalmol,1996

2. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis;Tham;Ophthalmology,2014

3. Ranking of optic disc variables for detection of glaucomatous optic nerve damage;Jonas;Invest Ophthalmol Vis Sci,2000

4. Reproducibility of nerve fiber thickness, macular thickness, and optic nerve head measurements using StratusOCT;Paunescu;Invest Ophthalmol Vis Sci,2004

5. Cup-to-disc ratio: agreement between slit-lamp indirect ophthalmoscopic estimation and stratus optical coherence tomography measurement;Arnalich-Montiel;Eye,2007

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