Ophthalmology

Macular Vessel Density and Ganglion Cell/Inner Plexiform Layer Thickness and Their Combinational Index Using Artificial Intelligence.

PURPOSE: To evaluate the relationship between macular vessel density and ganglion cell to inner plexiform layer thickness (GCIPLT) and to compare their diagnostic performance. We attempted to develop a new combined parameter using an artificial neural network.

Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD.

Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. H...

Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence.

PURPOSE: While millions of individuals show early age-related macular degeneration (AMD) signs, yet have excellent vision, the risk of progression to ...

Deep-learning Classifier With an Ultrawide-field Scanning Laser Ophthalmoscope Detects Glaucoma Visual Field Severity.

PURPOSE: To evaluate the accuracy of detecting glaucoma visual field defect severity using deep-learning (DL) classifier with an ultrawide-field scann...

Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation.

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and dia...

Fusing Results of Several Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema in Optical Coherence Tomography.

Diabetic Macular Edema (DME) is a severe eye disease that can lead to irreversible blindness if it is left untreated. DME diagnosis still relies on ma...

A New and Improved Method for Automated Screening of Age-Related Macular Degeneration Using Ensemble Deep Neural Networks.

In this paper, we provide a new framework on deep learning based automated screening method for finding individuals at risk of developing Age-related ...

Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading.

Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and automatic retinal image analysis algorithm for th...

High Intraocular Pressure Detection from Frontal Eye Images: A Machine Learning Based Approach.

This paper presents a novel framework to detect the status of intraocular pressure (normal/high) using solely frontal eye image analysis. The framewor...

A Unified Optic Nerve Head and Optic Cup Segmentation Using Unsupervised Neural Networks for Glaucoma Screening.

Segmentation of retinal anatomical features such as optic nerve head (ONH) and optic cup, the brightest area in the center of ONH which is devoid of n...

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

PURPOSE: To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related...

GPU-based deep convolutional neural network for tomographic phase microscopy with ℓ1 fitting and regularization.

Tomographic phase microscopy (TPM) is a unique imaging modality to measure the three-dimensional refractive index distribution of transparent and semi...

Deep Learning for Predicting Refractive Error From Retinal Fundus Images.

PURPOSE: We evaluate how deep learning can be applied to extract novel information such as refractive error from retinal fundus imaging.

Deep learning applications in ophthalmology.

PURPOSE OF REVIEW: To describe the emerging applications of deep learning in ophthalmology.

Machine Learning Based Automatic Neovascularization Detection on Optic Disc Region.

In this paper, the automatic detection of neovascularization in the optic disc region (NVD) for color fundus retinal image is presented. NV is the ind...

Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble.

Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical...

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.

The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establ...

Using machine learning to detect events in eye-tracking data.

Event detection is a challenging stage in eye movement data analysis. A major drawback of current event detection methods is that parameters have to b...

Scanpath modeling and classification with hidden Markov models.

How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed th...

Automated System for Referral of Cotton-Wool Spots.

BACKGROUND: Cotton-wool spots also referred as soft exudates are the early signs of complications in the eye fundus of the patients suffering from dia...