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An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging.

Siamak Yousefi1,2, Xiaoqin Huang1, Asma Poursoroush1

  • 1Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee.

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An objective glaucoma damage classification system was developed using optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness measurements. This system accurately differentiates normal eyes from early, moderate, and advanced glaucoma stages based on RNFL loss.

Keywords:
Artificial intelligenceGlaucomaGlaucoma severity damageOptical coherence tomographyRetinal nerve fiber layer (RNFL)StagingUnsupervised machine learning

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Machine Learning

Background:

  • Glaucoma is a leading cause of irreversible blindness.
  • Accurate glaucoma damage severity classification is crucial for patient management.
  • Current methods may lack objectivity and consistency.

Purpose of the Study:

  • To develop an objective glaucoma damage severity classification system.
  • Utilize optical coherence tomography (OCT)-derived retinal nerve fiber layer (RNFL) thickness measurements.
  • Establish a classification system based on unsupervised learning.

Main Methods:

  • Developed an unsupervised k-means model using multicenter OCT data.
  • Clustered eyes based on circumpapillary RNFL thickness profiles.
  • Determined optimal RNFL thickness thresholds using Bayes' minimum error principle for severity classification.

Main Results:

  • Identified four distinct clusters representing different RNFL thickness profiles.
  • Established optimal global RNFL thickness thresholds: >95 μm (normal), 86-95 μm (early), 70-85 μm (moderate), <70 μm (advanced).
  • Achieved high accuracy in classifying glaucoma severity, with 98% of advanced cases correctly identified.

Conclusions:

  • An objective, unbiased, and consistent glaucoma damage classification system was developed.
  • The system utilizes OCT-derived RNFL thickness measurements and unsupervised machine learning.
  • This classification method can enhance glaucoma research and clinical practice.