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Corneal thickness measurement by secondary speckle tracking and image processing using machine-learning algorithms.

Aviya Bennett1, Elnatan Davidovitch1, Yafim Beiderman1

  • 1Bar-Ilan University, Faculty of Engineering, Nanotechnology Center, Ramat-Gan, Israel.

Journal of Biomedical Optics
|December 5, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new, fast, and accurate method for measuring corneal thickness (CoT) using machine learning and laser speckle patterns. This technique precisely determines eye CoT, aiding in diagnosing eye conditions and assessing intraocular pressure.

Keywords:
corneal thicknessimaginglasersmachine learningopticssecondary speckle patterns

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Area of Science:

  • Ophthalmology
  • Biomedical Optics
  • Machine Learning in Healthcare

Background:

  • Corneal thickness (CoT) is crucial for diagnosing various eye disorders.
  • Accurate measurement of CoT is essential for assessing intraocular pressure.
  • Existing methods for CoT measurement have limitations in speed or precision.

Purpose of the Study:

  • To introduce a novel, high-precision method for measuring corneal thickness.
  • To leverage secondary speckle tracking and machine learning for CoT assessment.
  • To evaluate the accuracy and speed of the proposed technique.

Main Methods:

  • Capturing laser beam speckle patterns backscattered from the corneal-scleral border using a high-speed camera.
  • Processing speckle pattern images with machine learning (ML) algorithms.
  • Validating the technique on phantoms of varying thicknesses and in human clinical trials.

Main Results:

  • The developed method demonstrated high accuracy in determining corneal thickness.
  • The technique proved to be significantly faster compared to existing CoT measurement methods.
  • Successful implementation and validation in both phantom and human eye studies.

Conclusions:

  • The proposed laser speckle tracking and ML-based method offers a precise and rapid approach for corneal thickness measurement.
  • This technique holds potential for improved clinical evaluation of eye disorders and intraocular pressure assessment.
  • The speed and accuracy of this method suggest a valuable advancement in ophthalmic diagnostics.