Glaucoma

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 screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance ...

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 network (CNN) with optical coherence tomography (OCT) images and adjusting the values with Humphrey Field Analyzer (HFA) 24-2 test. The training dataset included 558 eyes from 312 glaucoma patients and 90 eyes from 46 normal subjects. The testing dataset ...

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...

ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach.

Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degen...

Visual Field Estimation by Probabilistic Classification.

The gold standard clinical tool for evaluating visual dysfunction in cases of glaucoma and other disorders of vision remains the visual field or thres...

Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images.

The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and dis...

Machine Learning Techniques for Ophthalmic Data Processing: A Review.

Machine learning and especially deep learning techniques are dominating medical image and data analysis. This article reviews machine learning approac...

Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning.

The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomogra...

Artificial intelligence (AI) impacting diagnosis of glaucoma and understanding the regulatory aspects of AI-based software as medical device.

Glaucoma, the group of eye diseases is characterized by increased intraocular pressure, optic neuropathy and visual field defect patterns. Early and c...

Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.

We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch's membrane opening-minimum rim width (BMO-MRW), peripap...

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.

PURPOSE: This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting gl...

Deep learning in glaucoma with optical coherence tomography: a review.

Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, par...

A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability.

Health data that are publicly available are valuable resources for digital health research. Several public datasets containing ophthalmological imagin...

Deep-learning-based enhanced optic-disc photography.

Optic-disc photography (ODP) has proven to be very useful for optic nerve evaluation in glaucoma. In real clinical practice, however, limited patient ...