AIMC Topic: Corneal Topography
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Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis.
Cornea
Feb 1, 2024
PURPOSE: The prevalence of keratoconus in the general population is reported to be up to 1 of 84. Over the past 2 decades, diagnosis and management evolved rapidly, but keratoconus screening in clinical practice is still challenging and asks for impr...
Deep Learning Based Prediction of Myopia Control Effect in Children Treated With Overnight Orthokeratology.
Eye & contact lens
Nov 7, 2023
OBJECTIVES: To develop and validate a deep learning-based model for predicting 12-month axial length (AL) elongation using baseline factors and early corneal topographic changes in children treated with orthokeratology (Ortho-K) and to investigate th...
Novel deep learning approach to estimate rigid gas permeable contact lens base curve for keratoconus fitting.
Contact lens & anterior eye : the journal of the British Contact Lens Association
Sep 28, 2023
INTRODUCTION: Rigid gas permeable contact lenses (RGP) are the most efficient means of providing optimal vision in keratoconus. RGP fitting can be challenging and time-consuming for ophthalmologists and patients. Deep learning predictive models could...
Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Jul 7, 2023
PURPOSE: This review was designed to compare different corneal imaging modalities using artificial intelligence (AI) for the diagnosis of keratoconus (KCN), subclinical KCN (SKCN), and forme fruste KCN (FFKCN).
KeratoScreen: Early Keratoconus Classification With Zernike Polynomial Using Deep Learning.
Cornea
Apr 20, 2022
PURPOSE: We aimed to investigate the usefulness of Zernike coefficients (ZCs) for distinguishing subclinical keratoconus (KC) from normal corneas and to evaluate the goodness of detection of the entire corneal topography and tomography characteristic...
Artificial intelligence applications in different imaging modalities for corneal topography.
Survey of ophthalmology
Aug 25, 2021
Interpretation of topographical maps used to detect corneal ectasias requires a high level of expertise. Several artificial intelligence (AI) technologies have attempted to interpret topographic maps. The purpose of this study is to provide a review ...
Artificial Intelligence Efficiently Identifies Regional Differences in the Progression of Tomographic Parameters of Keratoconic Corneas.
Journal of refractive surgery (Thorofare, N.J. : 1995)
Apr 1, 2021
PURPOSE: To develop an artificial intelligence (AI) model to effectively assess local versus global progression of keratoconus using multiple tomographic parameters.
Unsupervised learning for large-scale corneal topography clustering.
Scientific reports
Oct 12, 2020
Machine learning algorithms have recently shown their precision and potential in many different use cases and fields of medicine. Most of the algorithms used are supervised and need a large quantity of labeled data to achieve high accuracy. Also, mos...
Keratoconus Screening Based on Deep Learning Approach of Corneal Topography.
Translational vision science & technology
Sep 25, 2020
PURPOSE: To develop and compare deep learning (DL) algorithms to detect keratoconus on the basis of corneal topography and validate with visualization methods.
Corneal Topography Raw Data Classification Using a Convolutional Neural Network.
American journal of ophthalmology
Jun 10, 2020
PURPOSE: We investigated the efficiency of a convolutional neural network applied to corneal topography raw data to classify examinations of 3 categories: normal, keratoconus (KC), and history of refractive surgery (RS).
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