Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography Vrije Universiteit Brussel University of Antwerp
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.
METHODS: Pentacam data from 860 eyes were included in the study and divided into 5 groups: 454 KC, 67 forme fruste (FF), 28 astigmatic, 117 ...