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Glaucoma

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Patient-attentive sequential strategy for perimetry-based visual field acquisition.

Medical image analysis
Perimetry is a non-invasive clinical psychometric examination used for diagnosing ophthalmic and neurological conditions. At its core, perimetry relies on a subject pressing a button whenever they see a visual stimulus within their field of view. Thi...

CNNs for automatic glaucoma assessment using fundus images: an extensive validation.

Biomedical engineering online
BACKGROUND: Most current algorithms for automatic glaucoma assessment using fundus images rely on handcrafted features based on segmentation, which are affected by the performance of the chosen segmentation method and the extracted features. Among ot...

Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment.

IEEE transactions on medical imaging
Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database, the goal is to train a powerful classifie...

Feasibility of simple machine learning approaches to support detection of non-glaucomatous visual fields in future automated glaucoma clinics.

Eye (London, England)
OBJECTIVES: To assess the performance of feed-forward back-propagation artificial neural networks (ANNs) in detecting field defects caused by pituitary disease from among a glaucomatous population.

Machine learning models based on the dimensionality reduction of standard automated perimetry data for glaucoma diagnosis.

Artificial intelligence in medicine
INTRODUCTION: Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning techniques to the visual field test results, a valid clinical diagnosis of glaucoma solely based on the ...

Evaluation of deep convolutional neural networks for glaucoma detection.

Japanese journal of ophthalmology
PURPOSE: To investigate the performance of deep convolutional neural networks (DCNNs) for glaucoma discrimination using color fundus images STUDY DESIGN: A retrospective study PATIENTS AND METHODS: To investigate the discriminative ability of 3 DCNNs...

Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation.

IEEE transactions on medical imaging
Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and optic cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising...

Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.

Journal of healthcare engineering
This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous an...

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

IEEE journal of biomedical and health informatics
Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central regi...