Ophthalmology

Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model.

The application of deep learning techniques has led to substantial progress in solving a number of critical problems in machine vision, including fundamental problems of scene segmentation and depth estimation. Here, we report a novel deep neural network model, capable of simultaneous scene segmentation and depth estimation from a pair of binocular images. By manipulating the arrangement of binocu...

Multicolor image classification using the multimodal information bottleneck network (MMIB-Net) for detecting diabetic retinopathy.

Multicolor (MC) imaging is an imaging modality that records confocal scanning laser ophthalmoscope (cSLO) fundus images, which can be used for the diabetic retinopathy (DR) detection. By utilizing this imaging technique, multiple modal images can be obtained in a single case. Additional symptomatic features can be obtained if these images are considered during the diagnosis of DR. However, few stu...

Individualized Glaucoma Change Detection Using Deep Learning Auto Encoder-Based Regions of Interest.

PURPOSE: To compare change over time in eye-specific optical coherence tomography (OCT) retinal nerve fiber layer (RNFL)-based region-of-interest (ROI...

An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography.

Glaucoma is a chronic ocular degenerative disease that can cause blindness if left untreated in its early stages. Deep Convolutional Neural Networks (...

Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.

OBJECTIVE: Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard col...

[Deep learning based lesion detection from anterior segment optical coherence tomography images and its application in the diagnosis of keratoconus].

To developed an image analysis system of anterior segment optical coherence tomography (AS-OCT) examination results based on deep learning technology...

[Research advances of morphological changes of the choroid in high myopia].

Choroidal thinning is an important feature of high myopia and has a negative correlation with the degree of myopia. However, due to the limitations of...

T-able: An Investigation of Habituating Moving Tables at Home.

This paper investigates the philosophical and designerly questions of how the concepts of familiarity, orientation, and habituation can be used to und...

ANALYSIS OF FLUID VOLUME AND ITS IMPACT ON VISUAL ACUITY IN THE FLUID STUDY AS QUANTIFIED WITH DEEP LEARNING.

PURPOSE: To investigate quantitative differences in fluid volumes between subretinal fluid (SRF)-tolerant and SRF-intolerant treat-and-extend regimens...

Macular Ischemia Quantification Using Deep-Learning Denoised Optical Coherence Tomography Angiography in Branch Retinal Vein Occlusion.

PURPOSE: To examine whether deep-learning denoised optical coherence tomography angiography (OCTA) images could enhance automated macular ischemia qua...

Rapid classification of glaucomatous fundus images.

We propose a new method for training convolutional neural networks (CNNs) and use it to classify glaucoma from fundus images. This method integrates r...

Automatic Recording of the Target Location During Smooth Pursuit Eye Movement Testing Using Video-Oculography and Deep Learning-Based Object Detection.

PURPOSE: To accurately record the movements of a hand-held target together with the smooth pursuit eye movements (SPEMs) elicited with video-oculograp...

Imaging Biomarkers of 1-Year Activity in Type 1 Macular Neovascularization.

PURPOSE: The purpose of this study was to evaluate the predictive value of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters at...

DETECTION OF MORPHOLOGIC PATTERNS OF DIABETIC MACULAR EDEMA USING A DEEP LEARNING APPROACH BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES.

PURPOSE: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (O...

Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study.

BACKGROUND: By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically signifi...

High-dimensional profiling clusters asthma severity by lymphoid and non-lymphoid status.

Clinical definitions of asthma fail to capture the heterogeneity of immune dysfunction in severe, treatment-refractory disease. Applying mass cytometr...

Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans.

PURPOSE: To develop and evaluate methods to improve the generalizability of convolutional neural networks (CNNs) trained to detect glaucoma from optic...

Automated Quantification of Pathological Fluids in Neovascular Age-Related Macular Degeneration, and Its Repeatability Using Deep Learning.

PURPOSE: To develop a reliable algorithm for the automated identification, localization, and volume measurement of exudative manifestations in neovasc...

A Deep Learning Model for Screening Multiple Abnormal Findings in Ophthalmic Ultrasonography (With Video).

PURPOSE: The purpose of this study was to construct a deep learning system for rapidly and accurately screening retinal detachment (RD), vitreous deta...

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease.

Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of physiology and vascular diseases in microcirculation...