Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus
Open Access
- 1 November 2021
- journal article
- research article
- Published by Elsevier in Computers in Biology and Medicine
- Vol. 138, 104884
Abstract
No abstract availableKeywords
Funding Information
- State Government of Victoria
- National Health and Medical Research Council (1138585)
- National Health and Medical Research Council (APP1187763)
This publication has 23 references indexed in Scilit:
- Validation of an Objective Scoring System for Forme Fruste Keratoconus Detection and Post-LASIK Ectasia Risk Assessment in Asian EyesCornea, 2015
- The Genetic and Environmental Factors for KeratoconusBioMed Research International, 2015
- Assessment of Anterior Segment Parameters of Keratoconus Eyes in an Australian PopulationOptometry and Vision Science, 2014
- Analysis of Waveform-Derived ORA Parameters in Early Forms of Keratoconus and Normal CorneasJournal of Refractive Surgery, 2013
- Detection of Subclinical Keratoconus Using an Automated Decision Tree ClassificationAmerican Journal of Ophthalmology, 2013
- Anterior segment characteristics of keratoconus eyes in a sample of Asian populationContact Lens and Anterior Eye, 2013
- Characterization of corneal structure in keratoconusJournal of Cataract & Refractive Surgery, 2012
- Topographic and Tomographic Properties of Forme Fruste Keratoconus CorneasInvestigative Ophthalmology & Visual Science, 2010
- Building Predictive Models inRUsing thecaretPackage2008
- Evaluation of anterior segment parameters in keratoconic eyes measured with the Pentacam systemJournal of Cataract & Refractive Surgery, 2007