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

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

Research output: Contribution to journalReview article


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
Issue number4
StatePublished - May 19 2019



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

ASJC Scopus subject areas

  • Ophthalmology

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