Glaucoma

Ocular blood flow as it relates to race and disease on glaucoma.

Glaucoma is a multifactorial progressive and degenerative optic neuropathy representing one of the world's leading cause of irreversible blindness. Currently, reduction of intraocular pressure remains the only universally approved therapy, yet a wealth of studies has identified significant vascular contributions to the disease process in certain individuals. Population-based studies have identifie...

Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression.

OBJECTIVE: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ratios from fundus and optical coherence tomography scans. However, this paper presents a novel strategy that pays attention to the RGC atrophy for sc...

Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.

Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we...

Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA.

Cupping of the optic nerve head, a highly heritable trait, is a hallmark of glaucomatous optic neuropathy. Two key parameters are vertical cup-to-disc...

Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning.

PURPOSE: The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise...

Automatic glaucoma detection based on transfer induced attention network.

BACKGROUND: Glaucoma is one of the causes that leads to irreversible vision loss. Automatic glaucoma detection based on fundus images has been widely ...

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning.

OBJECTIVE: To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visua...

Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks.

: This study developed and evaluated a deep learning ensemble method to automatically grade the stages of glaucoma depending on its severity.: After c...

Hierarchical deep learning models using transfer learning for disease detection and classification based on small number of medical images.

Deep learning is being employed in disease detection and classification based on medical images for clinical decision making. It typically requires la...

Glaucoma Expert-Level Detection of Angle Closure in Goniophotographs With Convolutional Neural Networks: The Chinese American Eye Study.

PURPOSE: To compare the performance of a novel convolutional neural network (CNN) classifier and human graders in detecting angle closure in EyeCam (C...

Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.

A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, which is often expensive and time consuming to collect...

Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.

BACKGROUND: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma ...

Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images.

We aimed to develop a model to predict visual field (VF) in the central 10 degrees in patients with glaucoma, by training a convolutional neural netwo...

A combined convolutional and recurrent neural network for enhanced glaucoma detection.

Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g.,...

An objective structural and functional reference standard in glaucoma.

The current lack of consensus for diagnosing glaucoma makes it difficult to develop diagnostic tests derived from deep learning (DL) algorithms. In th...

Aqueous autotaxin and TGF-βs are promising diagnostic biomarkers for distinguishing open-angle glaucoma subtypes.

The purpose of this study is to examine if aqueous autotaxin (ATX) and TGF-β levels could be used for differentiating glaucoma subtypes. This prospect...

A novel retinal ganglion cell quantification tool based on deep learning.

Glaucoma is a disease associated with the loss of retinal ganglion cells (RGCs), and remains one of the primary causes of blindness worldwide. Major r...

Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

PURPOSE: To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fund...

Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.

PURPOSE: Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better ...

Applications of deep learning in detection of glaucoma: A systematic review.

Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the dis...