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KeratoScreen: Early Keratoconus Classification With Zernike Polynomial Using Deep Learning.

He-Bei Gao1,2, Zhi-Geng Pan3, Mei-Xiao Shen4

  • 1Division of Health Sciences, Hangzhou Normal University, Hangzhou, China.

Cornea
|May 11, 2022
PubMed
Summary
This summary is machine-generated.

Zernike coefficients (ZCs) effectively distinguish subclinical keratoconus (KC) from normal corneas using artificial neural networks. This deep learning approach shows high sensitivity and precision for early KC detection in preoperative screening.

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Keratoconus (KC) is a progressive corneal ectasia. Early detection of subclinical KC is crucial for preventing vision loss.
  • Accurate differentiation between normal corneas, subclinical KC, and KC is challenging with traditional methods.

Purpose of the Study:

  • To assess the utility of Zernike coefficients (ZCs) in differentiating subclinical KC from normal corneas.
  • To evaluate the performance of artificial neural networks (ANNs) using ZCs as input for corneal topography and tomography analysis.

Main Methods:

  • A retrospective study analyzed 1040 corneal topography images from 208 patients using the Pentacam system.
  • An ANN (KeratoScreen) was trained with ZCs derived from corneal topography and tomography data.
  • Data included normal (70 eyes), subclinical KC (48 eyes), and KC (90 eyes) groups, randomly split for training (70%) and testing (30%).

Main Results:

  • ZCs derived from corneal thickness demonstrated the best performance in classifying the three groups.
  • High sensitivity (93.9%) and precision (96.1%) were achieved for subclinical KC detection.
  • Excellent sensitivity (97.6%) and precision (95.1%) were observed for KC detection.

Conclusions:

  • Deep learning algorithms utilizing ZCs show significant promise for screening early-stage KC and other corneal ectasias.
  • This AI-driven approach can be valuable for preoperative screening in refractive surgery patients.
  • ZCs provide a robust feature set for AI-based analysis of corneal topography and tomography.