AIMC Topic: Cornea
Clear Filters Showing 51 to 60 of 94 articles
A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.
PloS one
Jun 3, 2021
PURPOSE: Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in compute...
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.
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.
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.
A deep learning approach in diagnosing fungal keratitis based on corneal photographs.
Scientific reports
Sep 2, 2020
Fungal keratitis (FK) is the most devastating and vision-threatening microbial keratitis, but clinical diagnosis a great challenge. This study aimed to develop and verify a deep learning (DL)-based corneal photograph model for diagnosing FK. Corneal ...
A Deep Learning Model for Automated Sub-Basal Corneal Nerve Segmentation and Evaluation Using In Vivo Confocal Microscopy.
Translational vision science & technology
Jun 18, 2020
PURPOSE: The purpose of this study was to establish a deep learning model for automated sub-basal corneal nerve fiber (CNF) segmentation and evaluation with in vivo confocal microscopy (IVCM).
The Role of Corneal Biomechanics for the Evaluation of Ectasia Patients.
International journal of environmental research and public health
Mar 23, 2020
PURPOSE: To review the role of corneal biomechanics for the clinical evaluation of patients with ectatic corneal diseases.
[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...
An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study.
Diabetologia
Nov 12, 2019
AIMS/HYPOTHESIS: Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either ...
CNN-based diagnosis models for canine ulcerative keratitis.
Scientific reports
Oct 2, 2019
The purpose of this methodological study was to develop a convolutional neural network (CNN), which is a recently developed deep-learning-based image recognition method, to determine corneal ulcer severity in dogs. The CNN model was trained with imag...
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