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Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis.

Publication ,  Journal Article
Grewal, DS; Jain, R; Grewal, SPS; Rihani, V
Published in: Eur J Ophthalmol
2008

PURPOSE: To develop, train, and test an artificial neural network (ANN) for differentiating among normal subjects, primary open angle glaucoma (POAG) suspects, and persons with POAG in Asian-Indian eyes using inputs from clinical parameters, optical coherence tomography (OCT), visual fields, and GDx nerve fiber analyzer. METHODS: One hundred eyes were classified using optic disc examination and perimetry into normal (n=35), POAG suspects (n=30), and POAG (n=35). EasyNN-plus simulator was used to develop an ANN model with inputs including age, sex, myopia, intraocular pressure (IOP), optic nerve head, and retinal nerve fiber layer (RNFL) parameters on OCT, Octopus 30-2 full threshold visual field, and GDx parameters. RESULTS: With two outputs (POAG or normal), specificity was 80% and sensitivity was 93.3%. Ninety percent of POAG suspects were labeled as abnormal in this analysis. ANN assigned the highest importance to Smax/Imax RNFL on OCT followed by cup-area (OCT) and other RNFL parameters (OCT) for two outputs. With three outputs (normal, POAG, and POAG suspect), ANN gave an overall classification rate of 65%, specificity of 60%, and sensitivity of 71.4% with a target error rate of the training set at 1%. The parameters for three outputs, in decreasing order of relative importance, were Savg, vertical cup-disc ratio, cup-volume, and cup-area on OCT. CONCLUSIONS: An ANN taking varied diagnostic imaging inputs was able to separate POAG eyes from normal subjects and POAG suspects. The network had reasonable sensitivity with three outputs; however, it had a tendency to mislabel POAG suspects as POAG.

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Published In

Eur J Ophthalmol

DOI

ISSN

1120-6721

Publication Date

2008

Volume

18

Issue

6

Start / End Page

915 / 921

Location

United States

Related Subject Headings

  • Visual Fields
  • Vision Disorders
  • Tomography, Optical Coherence
  • Sensitivity and Specificity
  • Retinal Ganglion Cells
  • Optic Nerve Diseases
  • Optic Disk
  • Ophthalmology & Optometry
  • Ocular Hypertension
  • Neural Networks, Computer
 

Citation

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Chicago
ICMJE
MLA
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Grewal, D. S., Jain, R., Grewal, S. P. S., & Rihani, V. (2008). Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis. Eur J Ophthalmol, 18(6), 915–921. https://doi.org/10.1177/112067210801800610
Grewal, D. S., R. Jain, S. P. S. Grewal, and V. Rihani. “Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis.Eur J Ophthalmol 18, no. 6 (2008): 915–21. https://doi.org/10.1177/112067210801800610.
Grewal DS, Jain R, Grewal SPS, Rihani V. Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis. Eur J Ophthalmol. 2008;18(6):915–21.
Grewal, D. S., et al. “Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis.Eur J Ophthalmol, vol. 18, no. 6, 2008, pp. 915–21. Pubmed, doi:10.1177/112067210801800610.
Grewal DS, Jain R, Grewal SPS, Rihani V. Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis. Eur J Ophthalmol. 2008;18(6):915–921.

Published In

Eur J Ophthalmol

DOI

ISSN

1120-6721

Publication Date

2008

Volume

18

Issue

6

Start / End Page

915 / 921

Location

United States

Related Subject Headings

  • Visual Fields
  • Vision Disorders
  • Tomography, Optical Coherence
  • Sensitivity and Specificity
  • Retinal Ganglion Cells
  • Optic Nerve Diseases
  • Optic Disk
  • Ophthalmology & Optometry
  • Ocular Hypertension
  • Neural Networks, Computer