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An objective structural and functional reference standard in glaucoma.

Eduardo B Mariottoni1,2, Alessandro A Jammal1, Samuel I Berchuck1,3

  • 1Vision, Imaging and Performance (VIP) Laboratory, Department of Ophthalmology, Duke Eye Center, Duke University, 2351 Erwin Rd, Durham, NC, 27701, USA.

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|January 19, 2021
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Summary
This summary is machine-generated.

Researchers developed an objective definition for glaucomatous optic neuropathy (GON) to train a deep learning (DL) algorithm. This DL algorithm accurately detects GON from fundus photos, improving diagnostic consistency for glaucoma.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma diagnosis lacks consensus, hindering development of deep learning (DL) diagnostic tools.
  • Objective diagnostic criteria are needed for reliable glaucoma assessment.

Purpose of the Study:

  • To propose an objective definition for glaucomatous optic neuropathy (GON).
  • To develop and evaluate a DL algorithm for detecting GON using fundus photos based on the new definition.

Main Methods:

  • Defined GON using parameters from optical coherence tomography and standard automated perimetry.
  • Trained a DL algorithm on fundus photos with the objective GON definition as the reference standard.
  • Evaluated algorithm performance using sensitivity, specificity, AUC, and likelihood ratios on an independent dataset.

Main Results:

  • The DL algorithm achieved an AUC of 0.92 for discriminating between GON and normal eyes.
  • The algorithm demonstrated 77% sensitivity at 95% specificity.
  • Likelihood ratios indicated significant changes in post-test disease probability.

Conclusions:

  • A DL algorithm can effectively detect GON from fundus photos with high performance.
  • The proposed objective definition of GON enhances the comparability of glaucoma diagnostic studies.
  • This approach may standardize glaucoma diagnosis across different devices and populations.