June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
A neural network based retinal nerve fiber layer profile compensation to improve the discrimination ability of glaucoma in high myopia in population
Author Affiliations & Notes
  • Ya Xing Wang
    Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing, China
  • Lei Li
    State Key Laboratory of Software Development Environment, Beihang University, China
  • Rahul Arvo Jonas
    Department of Ophthalmology, University of Cologne, Faculty of Medicine and University Hospital, Germany
  • Liang Xu
    Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing, China
  • David F Garwary-Heath
    NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, United Kingdom
  • Jost Jonas
    Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University of Heidelberg, Germany
  • Haogang Zhu
    State Key Laboratory of Software Development Environment, Beihang University, China
  • Footnotes
    Commercial Relationships   Ya Xing Wang, None; Lei Li, None; Rahul Jonas, None; Liang Xu, None; David Garwary-Heath, Carl Zeiss Meditec (C); Jost Jonas, Abyss Processing Co.. Songhomitra (C), Biocompatibles UK Ltd. (P), Europäische Patentanmeldung (P); Haogang Zhu, None
  • Footnotes
    Support  National Natural Science Foundation of China (number 81570835); State Key Laboratory of Software Development Environment, Beihang University, Beijing, China.
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4550. doi:
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      Ya Xing Wang, Lei Li, Rahul Arvo Jonas, Liang Xu, David F Garwary-Heath, Jost Jonas, Haogang Zhu; A neural network based retinal nerve fiber layer profile compensation to improve the discrimination ability of glaucoma in high myopia in population. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4550.

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

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Abstract

Purpose : To develop a neural network-based method for adjusting of the dependence of the peripapillary retinal nerve fiber layer (RNFL) profile on age, gender, and ocular biometric parameters, and to evaluate its performance in glaucoma diagnosis, especially in high myopia.

Methods : Participants were randomly selected from the population-based Beijing Eye Study 2011. A total of 2477 non-glaucomatous participants and 254 glaucoma patients were included. A detailed ophthalmic and systematic examination included RNFL profile measurement using spectral-domain optical coherence tomography, with 768 points at circumferential 3.4mm. In a test group of 2223 non-glaucomatous eyes, a fully connected network and radial basis function network were trained for the vertical (scaling) and horizontal (shift) transformation of the RNFL thickness profile with adjustment for age, axial length (AL), disc-fovea angle and disc-fovea distance. The performance of the RNFL thickness compensation was evaluated in an independent validation group of 254 glaucoma patients and 254 non-glaucomatous participants.

Results : Applying the RNFL compensation algorithm improved the area under the receiver operative characteristic curve in detecting glaucoma from 0.703 to 0.842, from 0.748 to 0.889, from 0.767 to 0.891, and from 0.779 to 0.867, for eyes in the highest 10% (mean: 26.0±0.9mm), 20% (25.3±1.0mm), and highest 30% (24.9±1.0mm) percentile subgroup of the AL distribution, and in the eyes of any AL (23.5±1.2mm), as compared with the unadjusted RNFL data, respectively. The difference between the uncompensated versus compensated RNFL thickness values expressed in relative percentage points increased with longer axial length: it increased by 19.8%, 18.9% and 16.2% in the highest 10%, 20% and 30% percentile subgroups, respectively.

Conclusions : In a population-based study sample, an algorithm-based adjustment for age, gender, and ocular biometric parameters improved the diagnostic precision of the peripapillary RNFL thickness profile for glaucoma detection in particular in myopic and highly myopic eyes.

This is a 2020 ARVO Annual Meeting abstract.

 

 

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