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Machine Learning Applied to Optometry Data

  • Beatriz RemeseiroEmail author
  • Noelia Barreira
  • Luisa Sánchez-Brea
  • Lucía Ramos
  • Antonio Mosquera
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 137)

Abstract

Optometry is the primary health care of the eye and visual system. It involves detecting defects in vision, signs of injury, ocular diseases as well as problems with general health that produce side effects in the eyes. Myopia, presbyopia, glaucoma or diabetic retinopathy are some examples of conditions that optometrists usually diagnose and treat. Moreover, there is another condition that we have all experienced once in a while, especially if we work with computers or have been exposed to smoke or wind. Dry eye syndrome (DES) is a hidden multifactorial disease related with the quality and quantity of tears. It causes discomfort and could lead to severe visual problems. In this chapter, we explain how machine learning techniques can be applied in some DES medical tests in order to produce an objective, repeatable and automatic diagnosis. The results of our experiments show that the proposed methodologies behave like the experts so that they can be applied in the daily practice.

Keywords

Optometry data Dry eye syndrome Image analysis Feature selection Classification Regression 

Notes

Acknowledgements

This work has been partially funded by the Ministerio de Economía y Competitividad of Spain (project DPI2015-69948-R). Beatriz Remeseiro acknowledges the support of the Ministerio de Economía y Competitividad of the Spanish Government under Juan de la Cierva Program (ref. FJCI-2014-21194).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Beatriz Remeseiro
    • 1
    Email author
  • Noelia Barreira
    • 2
  • Luisa Sánchez-Brea
    • 2
  • Lucía Ramos
    • 2
  • Antonio Mosquera
    • 3
  1. 1.Departament de Matemàtiques i InformàticaUniversitat de BarcelonaBarcelonaSpain
  2. 2.Departamento de ComputaciónUniversidade da CoruñaA CoruñaSpain
  3. 3.Departamento de Electrónica y ComputaciónUniversidade de Santiago de CompostelaSantiago de CompostelaSpain

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