AIMC Topic: Corneal Topography

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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
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
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
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
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)
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
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
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
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).

The Role of Corneal Biomechanics for the Evaluation of Ectasia Patients.

International journal of environmental research and public health
PURPOSE: To review the role of corneal biomechanics for the clinical evaluation of patients with ectatic corneal diseases.

Computer aided diagnosis for suspect keratoconus detection.

Computers in biology and medicine
PURPOSE: To develop a stable and low-cost computer aided diagnosis (CAD) system for early keratoconus detection for clinical use.