Ophthalmology

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

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 other characteristics, convolutional neural networks (CNNs) are known because of their ability to learn highly discriminative features from raw pixel in...

Posterior scleral deformations around optic disc are associated with visual field damage in open-angle glaucoma patients with myopia.

PURPOSE: To identify important variables associated with visual field (VF) defects in open-angle glaucoma (OAG) with myopia.

Deep learning-based super-resolution in coherent imaging systems.

We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demon...

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

Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, give...

Feasibility of support vector machine learning in age-related macular degeneration using small sample yielding sparse optical coherence tomography data.

PURPOSE: A retrospective pilot study is conducted to demonstrate the utility of a novel support vector machine learning (SVML) algorithm in a small th...

Partially spatially coherent digital holographic microscopy and machine learning for quantitative analysis of human spermatozoa under oxidative stress condition.

Semen quality assessed by sperm count and sperm cell characteristics such as morphology and motility, is considered to be the main determinant of men'...

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

OBJECTIVES: To assess the performance of feed-forward back-propagation artificial neural networks (ANNs) in detecting field defects caused by pituitar...

Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods.

In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascul...

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

INTRODUCTION: Visual field testing via standard automated perimetry (SAP) is a commonly used glaucoma diagnosis method. Applying machine learning tech...

Evaluation of deep convolutional neural networks for glaucoma detection.

PURPOSE: To investigate the performance of deep convolutional neural networks (DCNNs) for glaucoma discrimination using color fundus images STUDY DESI...

Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy.

PURPOSE: We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, ...

Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.

As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images...

Use of a robotic camera holder (FreeHand) for laparoscopic appendicectomy.

Use of a mechanical arm to hold the laparoscopic camera has many advantages. FreeHand (FreeHand Ltd, Guildford, United Kingdom) is a robotic camera h...

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

Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and optic cup (OC) from fundus images is beneficia...

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

This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional...

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

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 colo...

A bio-inspired sighted robot chases like a hoverfly.

Here we present a novel bio-inspired visual processing system, which enables a robot to locate and follow a target, using an artificial compound eye c...

Defining host-pathogen interactions employing an artificial intelligence workflow.

UNLABELLED: For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manual...

Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.

Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and...

Clinical applications of control systems models: The neural integrators for eye movements.

The first models that were proposed to account for the neural control of eye movements applied a classic control systems approach, including feedback,...