June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Assessment of risk of normal tension glaucoma progression via deep-learning based retinal-vessel caliber measurement
Author Affiliations & Notes
  • Timothy P.H. Lin
    Ophthalmology and Visual Sciences, The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
  • Herbert Y.H. Hui
    Ophthalmology and Visual Sciences, The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
  • Poemen P. Chan
    Ophthalmology and Visual Sciences, The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
    Hong Kong Eye Hospital, Hong Kong, Hong Kong
  • Yumeng Wang
    Ophthalmology and Visual Sciences, The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
  • Ruyue Shen
    Ophthalmology and Visual Sciences, The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
  • Tien Y Wong
    Singapore Eye Research Institute, Singapore, Singapore
  • Clement C. Tham
    Ophthalmology and Visual Sciences, The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
    Hong Kong Eye Hospital, Hong Kong, Hong Kong
  • Carol Y. Cheung
    Ophthalmology and Visual Sciences, The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
  • Footnotes
    Commercial Relationships   Timothy P.H. Lin, None; Herbert Y.H. Hui, None; Poemen P. Chan, None; Yumeng Wang, None; Ruyue Shen, None; Tien Wong, None; Clement C. Tham, None; Carol Y. Cheung, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 1027. doi:
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    • Get Citation

      Timothy P.H. Lin, Herbert Y.H. Hui, Poemen P. Chan, Yumeng Wang, Ruyue Shen, Tien Y Wong, Clement C. Tham, Carol Y. Cheung; Assessment of risk of normal tension glaucoma progression via deep-learning based retinal-vessel caliber measurement. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1027.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Vascular insufficiency has been suggested to be of pathogenic role in normal tension glaucoma (NTG). This study evaluated the associations between baseline retinal-vessel caliber measured by an automated deep-learning system to the risk of NTG progression.

Methods : In this longitudinal cohort study, 390 eyes from 253 NTG patients with a follow-up period of ≥24 months were included. Central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE) were measured from retinal photographs by a validated deep-learning system (SIVA-DLS). We assessed the agreement of retinal vessel caliber measurements between SIVA-DLS and humans, and used the Cox proportional-hazards model to examine the relationships between baseline retinal vessel caliber and subsequent NTG progression. We further assessed the incremental value of adding the retinal vessel caliber for prediction of NTG progression beyond previously reported risk factors. NTG progression was defined as progressive retinal nerve fiber layer (RNFL) thinning over time on Cirrus HD-OCT detected by its built-in guided progression analysis.

Results : Agreement of retinal vessel caliber measurement between SIVA-DLS and human was good to excellent, with intraclass correlation coefficients of retinal vessel caliber between SIVA-DLS and human ranging from 0.87 to 0.90. Sixty-nine eyes (17.7%) developed NTG progression over the follow-up period. Narrower CRAE (hazard ratio [HR] 1.36 [95%CI 1.05, 1.76]) and CRVE (HR 1.38 [95%CI 1.03, 1.84]) at baseline were independently associated with NTG progression in the multivariable model. Addition of baseline CRAE and CRVE improved the discrimination (C-statistic 0.703 vs 0.695, p=0.038 and 0.704 vs 0.695, p=0.038, respectively) of NTG progression risk beyond previously reported risk factors.

Conclusions : This study demonstrated significant associations between baseline retinal vessel caliber and risk of NTG progression. These findings provided evidence to support the hypothesis of vascular insufficiency in NTG pathogenesis and the utility of a high-throughput deep-learning system for risk assessment in NTG.

This is a 2021 ARVO Annual Meeting abstract.

 

CRAE and CRVE measurement using SIVA-DLS. The original retinal photograph captured by a 45 degree digital retinal camera (i). SIVA-DLS automatically generate heat maps based on the features of the retinal vessels for prediction and calculation of CRAE (ii) and CRVE (iii).

CRAE and CRVE measurement using SIVA-DLS. The original retinal photograph captured by a 45 degree digital retinal camera (i). SIVA-DLS automatically generate heat maps based on the features of the retinal vessels for prediction and calculation of CRAE (ii) and CRVE (iii).

 

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