Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity.

ABSTRACT:

Background

To investigate machine-learning (ML) algorithms to differentiate corneal biomechanical properties between different topographical stages of keratoconus (KC) by dynamic Scheimpflug tonometry (CST, Corvis ST, Oculus, Wetzlar, Germany). In the following, ML models were used to predict the severity in a training and validation dataset.

Methods

Three hundred and eighteen keratoconic and one hundred sixteen healthy eyes were included in this monocentric and cross-sectional pilot study. Dynamic corneal response (DCR) and corneal thickness related (pachymetric) parameters from CST were chosen by appropriated selection techniques to develop a ML algorithm. The stage of KC was determined by the topographical keratoconus classification system (TKC, Pentacam, Oculus). Patient

SUBMITTER: Herber R 

PROVIDER: S-EPMC8167942 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature