Detection of Subclinical Keratoconus Using an Automated Decision Tree Classification – Corrected Proof
Purpose: To develop a method for automatizing the detection of subclinical keratoconus based on a tree classification.Design: Retrospective case-control study.Methods: setting: University Hospital of Bordeaux. participants: A total of 372 eyes of 197 patients were enrolled: 177 normal eyes of 95 subjects, 47 eyes of 47 patients with forme fruste keratoconus, and 148 eyes of 102 patients with keratoconus. observation procedure: All eyes were imaged with a dual Scheimpflug analyzer. Fifty-five parameters derived from anterior and posterior corneal measurements were analyzed for each eye and a machine learning algorithm, the classification and regression tree, was used to classify the eyes (Read more...)