A Review of Machine Learning Techniques for Keratoconus Detection and Refractive Surgery Screening

Shawn R. Lin, John G. Ladas, Gavin G. Bahadur, Saba Al-Hashimi, Roberto Pineda

Research output: Contribution to journalReview articlepeer-review

Abstract

Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. These techniques utilize inputs from a range of corneal imaging devices and are built with automated decision trees, support vector machines, and various types of neural networks. In general, these techniques demonstrate very good differentiation of normal and keratoconic eyes, as well as good differentiation of normal and form fruste keratoconus. However, it is difficult to directly compare these studies, as keratoconus represents a wide spectrum of disease. More importantly, no public dataset exists for research purposes. Despite these challenges, machine learning in keratoconus detection and refractive surgery screening is a burgeoning field of study, with significant potential for continued advancement as imaging devices and techniques become more sophisticated.

Original languageEnglish (US)
Pages (from-to)317-326
Number of pages10
JournalSeminars in Ophthalmology
Volume34
Issue number4
DOIs
StatePublished - May 19 2019

Keywords

  • Machine learning
  • artificial intelligence
  • corneal ectasia
  • keratoconus
  • refractive surgery screening

ASJC Scopus subject areas

  • Ophthalmology

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