AIMC Topic: Dry Eye Syndromes
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Detecting dry eye from ocular surface videos based on deep learning.
The ocular surface
Jan 26, 2023
OBJECTIVE: To assess the performance of convolutional neural networks (CNNs) for automated diagnosis of dry eye (DE) in patients undergoing video keratoscopy based on single ocular surface video frames.
Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review.
Eye (London, England)
Nov 15, 2022
Corneal and ocular surface diseases (OSDs) carry significant psychosocial and economic burden worldwide. We set out to review the literature on the application of artificial intelligence (AI) and bioinformatics for analysis of biofluid biomarkers in ...
Automatic identification of meibomian gland dysfunction with meibography images using deep learning.
International ophthalmology
Sep 19, 2022
BACKGROUND: Artificial intelligence is developing rapidly, bringing increasing numbers of intelligent products into daily life. However, it has little progress in dry eye, which is a common disease and associated with meibomian gland dysfunction (MGD...
Automation of dry eye disease quantitative assessment: A review.
Clinical & experimental ophthalmology
Jun 27, 2022
Dry eye disease (DED) is a common eye condition worldwide and a primary reason for visits to the ophthalmologist. DED diagnosis is performed through a combination of tests, some of which are unfortunately invasive, non-reproducible and lack accuracy....
Automated quantification of meibomian gland dropout in infrared meibography using deep learning.
The ocular surface
Jun 24, 2022
PURPOSE: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.
A deep learning model established for evaluating lid margin signs with colour anterior segment photography.
Eye (London, England)
Jun 23, 2022
OBJECTIVES: To evaluate the feasibility of applying a deep learning model to identify lid margin signs from colour anterior segment photography.
Change patterns in the corneal sub-basal nerve and corneal aberrations in patients with dry eye disease: An artificial intelligence analysis.
Experimental eye research
Dec 10, 2021
We aimed to investigate the change patterns in corneal sub-basal nerve morphology and corneal intrinsic aberrations in dry eye disease (DED). Our study included 229 eyes of 155 patients with DED and 40 eyes of 20 healthy control. We used the Oculus k...
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...
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...
Dry eye is matched by increased intrasubject variability in tear osmolarity as confirmed by machine learning approach.
Archivos de la Sociedad Espanola de Oftalmologia
May 20, 2019
OBJECTIVE: Because of high variability, tear film osmolarity measures have been questioned in dry eye assessment. Understanding the origin of such variability would aid data interpretation. This study aims to evaluate osmolarity variability in a clin...
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