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Full-Field Optical Coherence Microscopy for Histology-Like Analysis of Stromal Features in Corneal Grafts

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Correlation between corneal dynamic responses and keratoconus topographic parameters.

Hsi-Yun Tai¹, Jun-Ji Lin¹, Yi-Hung Huang¹

¹Department of Mechanical Engineering, National Taiwan University, Taipei.

The Journal of International Medical Research

|June 29, 2022

Summary

Machine learning reveals strong correlations between corneal biomechanics and topography in keratoconus patients. This aids in automatic diagnosis and understanding disease progression.

Keywords:

Corneal dynamic characteristics Corvis ST keratoconus machine learning

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Area of Science:

Ophthalmology
Biomedical Engineering
Data Science

Background:

Keratoconus is a progressive thinning of the cornea.
Accurate diagnosis and severity assessment are crucial for effective management.
Corneal biomechanical properties offer insights into corneal health.

Purpose of the Study:

To explore the relationship between corneal biomechanics and topographic parameters in keratoconus.
To utilize machine learning for automatic diagnosis and severity grading of keratoconus.
To construct a reference benchmark for keratoconus severity.

Main Methods:

Retrospective analysis of 31 keratoconus patient eyes.
Application of two clustering approaches: shape-based and feature-based.
Utilizing a validated keratoconus benchmark and extracting key features for analysis.

Main Results:

Significant correlations identified between symmetric modes and keratoconus severity (PPMC=0.92).
Strong associations found between asymmetric modes and the location of the weak centroid (PPMC=0.75).
Machine learning effectively linked topographic signs with dynamic corneal behavior.

Conclusions:

Corneal topography and dynamic biomechanical behavior are interrelated in keratoconus.
Machine learning models show promise for objective keratoconus assessment.
Further research with larger datasets is needed to refine diagnostic benchmarks.