Ophthalmology

Artificial intelligence in glaucoma.

PURPOSE OF REVIEW: The use of computers has become increasingly relevant to medical decision-making, and artificial intelligence methods have recently demonstrated significant advances in medicine. We therefore provide an overview of current artificial intelligence methods and their applications, to help the practicing ophthalmologist understand their potential impact on glaucoma care.

Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.

IMPORTANCE: Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AM...

Screening Glaucoma With Red-free Fundus Photography Using Deep Learning Classifier and Polar Transformation.

UNLABELLED: PRéCIS:: The novel proposed algorithm using deep learning classifier and polar transformation technique can be an economical as well as an...

Learning From Peers' Eye Movements in the Absence of Expert Guidance: A Proof of Concept Using Laboratory Stock Trading, Eye Tracking, and Machine Learning.

Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. How...

Fiber bundle image restoration using deep learning.

We propose a deep learning-based restoration method to remove honeycomb patterns and improve resolution for fiber bundle (FB) images. By building and ...

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.

PURPOSE: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickenin...

[A machine learning model on orthokeratology lens fitting based on the data of optometry examination].

To get an orthokeratology lens fitting model according to the research of the optometry examination data, which can help to improve the work efficien...

Deep Learning for Prediction of AMD Progression: A Pilot Study.

PURPOSE: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degene...

An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

PURPOSE: To detect visual field (VF) progression by analyzing spatial pattern changes.

Applications of Deep Learning and Artificial Intelligence in Retina.

Evaluating the diameter of eyedropper tips using a computer vision system.

PURPOSE: This study aimed to determine the variation in diameters of outer and inner apertures of eyedropper tips using a computer vision system. Stan...

Vision-referential speech enhancement of an audio signal using mask information captured as visual data.

This paper describes a vision-referential speech enhancement of an audio signal using mask information captured as visual data. Smartphones and tablet...

Deep learning-Using machine learning to study biological vision.

Many vision science studies employ machine learning, especially the version called "deep learning." Neuroscientists use machine learning to decode neu...

Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.

Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-s...

Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular Degeneration.

IMPORTANCE: Although deep learning (DL) can identify the intermediate or advanced stages of age-related macular degeneration (AMD) as a binary yes or ...

Progress in Automated Deep Learning for Macular Degeneration.

PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging.

Aging biomarkers are the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important f...

Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration.

This study uses fundus images from a national data set to assess 2 deep learning methods for referability classification of age-related macular degene...

Spiking Neural Networks with Unsupervised Learning Based on STDP Using Resistive Synaptic Devices and Analog CMOS Neuron Circuit.

We designed the CMOS analog integrate and fire (I&F) neuron circuit can drive resistive synaptic device. The neuron circuit consists of a current mirr...

[Deep learning to support therapy decisions for intravitreal injections].

Significant progress has been made in artificial intelligence and computer vision research in recent years. Machine learning methods excel in a wide v...