AIMC Topic: Keratoconus

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Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms.

Computational and mathematical methods in medicine Nov 16, 2021
Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the ear...
Early Diagnosis Linear Models Decision Trees Keratoconus Bayes Theorem Databases, Factual Computer Simulation Cornea Logistic Models Support Vector Machine Severity of Illness Index Computational Biology Neural Networks, Computer Machine Learning Algorithms Humans
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KerNet: A Novel Deep Learning Approach for Keratoconus and Sub-Clinical Keratoconus Detection Based on Raw Data of the Pentacam HR System.

IEEE journal of biomedical and health informatics Oct 6, 2021
Keratoconus is one of the most severe corneal diseases, which is difficult to detect at the early stage (i.e., sub-clinical keratoconus) and possibly results in vision loss. In this paper, we propose a novel end-to-end deep learning approach, called ...
Neural Networks, Computer Cornea Deep Learning Humans 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...
Deep Learning Pilot Projects Keratoconus Cornea Male Middle Aged Humans Retrospective Studies Biometry Female Corneal Transplantation Aged Adult
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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 ...
Corneal Topography Cornea Humans ROC Curve Keratoconus Artificial Intelligence
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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.
Artificial Intelligence Retrospective Studies Cornea Keratoconus Humans Corneal Topography
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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...
Keratoconus Tomography, Optical Coherence Dry Eye Syndromes Deep Learning ROC Curve Area Under Curve Corneal Diseases Fuchs' Endothelial Dystrophy Neural Networks, Computer Prospective Studies Female Male Middle Aged Diagnosis, Computer-Assisted Humans
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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.
Humans Cornea Refractive Surgical Procedures Deep Learning Tomography Keratoconus
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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...
Information Storage and Retrieval Humans Corneal Topography Corneal Diseases Data Collection Cluster Analysis Algorithms Datasets as Topic Unsupervised Machine Learning Databases, Factual Keratoconus
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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.
Humans Corneal Topography Cornea Retrospective Studies Deep Learning Keratoconus
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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).
Adult Corneal Pachymetry Corneal Topography Sensitivity and Specificity Retrospective Studies Refractive Surgical Procedures Middle Aged Humans Male Healthy Volunteers Female Refractive Errors Neural Networks, Computer Reproducibility of Results Keratoconus Machine Learning
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