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

Journal: Seminars in ophthalmology
Published Date: Jan 1, 2019

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.

Authors

  • Shawn R Lin
    a Massachusetts Eye and Ear Infirmary , Harvard Medical School , Boston , MA , USA.
  • John G Ladas
    b Wilmer Eye Institute , Johns Hopkins Medical Institutions , Baltimore , MD , USA.
  • Gavin G Bahadur
    c Stein Eye Institute, David Geffen School of Medicine , University of California , Los Angeles , CA , USA.
  • Saba Al-Hashimi
    c Stein Eye Institute, David Geffen School of Medicine , University of California , Los Angeles , CA , USA.
  • Roberto Pineda
    a Massachusetts Eye and Ear Infirmary , Harvard Medical School , Boston , MA , USA.

Keywords

External Resources

Popular Topics
Recent Journals