Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.

Journal: Cornea

Published Date: Jun 1, 2016

Abstract

PURPOSE: To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with other known keratoconus (KC) classification methods.

Authors

Irene Ruiz Hidalgo

*Department of Ophthalmology, Antwerp University Hospital, Edegem, Belgium; †Department of Medicine and Health Sciences, Antwerp University, Wilrijk, Belgium; and ‡Visual Optics Group, Aragón Materials Science Institute (ICMA) Zaragoza, Consejo Superior de Investigaciones Científicas, University of Zaragoza, Spain.
Pablo Rodriguez
Jos J Rozema
Sorcha Ní Dhubhghaill
Nadia Zakaria
Marie-José Tassignon
Carina Koppen

Keywords

Adolescent Adult Algorithms Corneal Pachymetry Corneal Topography Diagnostic Techniques, Ophthalmological Female Humans Keratoconus Machine Learning Male Middle Aged Reproducibility of Results Retrospective Studies Sensitivity and Specificity Young Adult

External Resources

View on PubMed Access via DOI PubMed (27055215)

Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.

Abstract

Authors

Keywords

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