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Keratoconus severity identification using unsupervised machine learning.

Siamak Yousefi1,2, Ebrahim Yousefi1, Hidenori Takahashi3

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

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This study introduces an unsupervised machine learning algorithm to accurately identify and monitor keratoconus stages using corneal imaging data. The method effectively classifies eyes into distinct clusters representing different stages of the disease.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Machine Learning

Background:

  • Keratoconus is a progressive eye condition affecting corneal shape.
  • Accurate staging is crucial for timely intervention and management.
  • Current methods may have limitations in comprehensively assessing keratoconus severity.

Purpose of the Study:

  • To develop and validate an unsupervised machine learning algorithm for identifying and monitoring keratoconus stages.
  • To apply advanced data reduction techniques to a large dataset of corneal imaging parameters.
  • To objectively classify eyes into distinct keratoconus severity levels.

Main Methods:

  • Assembled a large dataset of 3,156 eyes from swept-source optical coherence tomography (OCT) images.
  • Utilized Principal Component Analysis (PCA) for linear dimensionality reduction from 420 to 8 principal components.
  • Applied manifold learning for nonlinear reduction to two eigen-parameters, followed by density-based clustering.

Main Results:

  • The algorithm successfully identified four distinct clusters representing different keratoconus stages.
  • Cluster I: Primarily normal eyes. Cluster II: Healthy and forme fruste keratoconus eyes.
  • Cluster III: Mild keratoconus. Cluster IV: Advanced keratoconus, validated by Ectasia Status Index (ESI).

Conclusions:

  • Unsupervised machine learning effectively identifies and visualizes keratoconus stages using corneal topography, elevation, and pachymetry data.
  • The algorithm provides a robust method for assessing keratoconus severity and progression.
  • This approach offers potential for improved clinical monitoring and diagnosis of keratoconus.