Detection of subclinical keratoconus using a novel combined tomographic and biomechanical model based on an automated decision tree.

ABSTRACT: Early detection of keratoconus is a crucial factor in monitoring its progression and making the decision to perform refractive surgery. The aim of this study was to use the decision tree technique in the classification and prediction of subclinical keratoconus (SKC). A total of 194 eyes (including 105 normal eyes and 89 with SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. The decision tree models were generated using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms based on the training database. The discriminating rules of the CART model selected metrics of the Belin/Ambrósio

SUBMITTER: Song P 

PROVIDER: S-EPMC8964676 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature