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Updated: Oct 10, 2025

Full-Field Optical Coherence Microscopy for Histology-Like Analysis of Stromal Features in Corneal Grafts
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Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review.

Howard Maile1, Ji-Peng Olivia Li2, Daniel Gore2

  • 1UCL Institute of Ophthalmology, University College London, London, United Kingdom.

JMIR Medical Informatics
|December 13, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning shows promise for detecting early keratoconus, a corneal disorder. Further research is needed to standardize algorithms and identify optimal parameters for accurate subclinical keratoconus detection.

Keywords:
artificial intelligencecorneacorneal diseasecorneal imagingcorneal tomographydecision support systemskeratoconuskeratometrymachine learningsubclinical

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Keratoconus is a progressive corneal thinning and distortion disorder.
  • Early detection via corneal collagen cross-linking can prevent vision loss.
  • Identifying subclinical keratoconus is challenging, necessitating advanced diagnostic tools.

Purpose of the Study:

  • To systematically review and evaluate literature on algorithmic detection of subclinical keratoconus.
  • To assess various machine learning algorithms and their definitions for early keratoconus detection.

Main Methods:

  • A systematic review of MEDLINE, Embase, Web of Science, and Cochrane Library databases (2010-2020).
  • Inclusion of full-text studies utilizing algorithms for subclinical keratoconus detection with validation.
  • Adherence to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

Main Results:

  • Analysis of 26 eligible papers comparing measured parameters and machine learning algorithm designs.
  • Detailed reporting of diagnostic criteria, demographics, sample size, acquisition systems, validation, parameter inputs, and algorithm outcomes.
  • Comprehensive comparison of various approaches for subclinical keratoconus identification.

Conclusions:

  • Machine learning holds potential for enhancing early keratoconus detection in clinical practice.
  • Lack of consensus exists on optimal corneal parameters and machine learning algorithm design.
  • Further research is recommended to refine early detection and patient stratification for timely treatment.