AIMC Topic: Keratoconus
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A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty.
Scientific reports
Sep 17, 2021
The efficacy of deep learning in predicting successful big-bubble (SBB) formation during deep anterior lamellar keratoplasty (DALK) was evaluated. Medical records of patients undergoing DALK at the University of Cologne, Germany between March 2013 an...
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.
Multidisease Deep Learning Neural Network for the Diagnosis of Corneal Diseases.
American journal of ophthalmology
Jan 30, 2021
PURPOSE: To report a multidisease deep learning diagnostic network (MDDN) of common corneal diseases: dry eye syndrome (DES), Fuchs endothelial dystrophy (FED), and keratoconus (KCN) using anterior segment optical coherence tomography (AS-OCT) images...
Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning.
Translational vision science & technology
Dec 18, 2020
PURPOSE: To assess the use of deep learning for high-performance image classification of color-coded corneal maps obtained using a Scheimpflug camera.
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).
[Artificial Intelligence for the Development of Screening Parameters in the Field of Corneal Biomechanics].
Klinische Monatsblatter fur Augenheilkunde
Dec 5, 2019
Machine learning and artificial intelligence are mostly important if data analysis by knowledge-based analytical methods is difficult and complex. In such cases, combined analytical and empirical approaches based on AI are also meaningful. The develo...
Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study.
BMJ open
Sep 27, 2019
OBJECTIVE: To evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT).
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