Ophthalmology

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 when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical data to assess the performance of a convolutional long short-term memory (LSTM) neural network.

Deep Learning Model for Accurate Automatic Determination of Phakic Status in Pediatric and Adult Ultrasound Biomicroscopy Images.

PURPOSE: Ultrasound biomicroscopy (UBM) is a noninvasive method for assessing anterior segment anatomy. Previous studies were prone to intergrader variability, lacked assessment of the lens-iris diaphragm, and excluded pediatric subjects. Lens status classification is an objective task applicable in pediatric and adult populations. We developed and validated a neural network to classify lens statu...

Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition.

In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the propose...

Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images.

BACKGROUND/AIMS: To apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions i...

Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants.

Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ dysfunction. This novel approach has potential to ...

Exploring a Structural Basis for Delayed Rod-Mediated Dark Adaptation in Age-Related Macular Degeneration Via Deep Learning.

PURPOSE: Delayed rod-mediated dark adaptation (RMDA) is a functional biomarker for incipient age-related macular degeneration (AMD). We used anatomica...

Detection of eye contact with deep neural networks is as accurate as human experts.

Eye contact is among the most primary means of social communication used by humans. Quantification of eye contact is valuable as a part of the analysi...

Performance Analysis of a Head and Eye Motion-Based Control Interface for Assistive Robots.

Assistive robots support people with limited mobility in their everyday life activities and work. However, most of the assistive systems and technolog...

A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs.

Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with pot...

DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.

PURPOSE: To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks.

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...

Robot-assisted versus stereotactic frame-based stereoelectroencephalography in medically refractory epilepsy.

AIM: To explore the difference between robot assisted (RA) and stereotactic frame based (SF) stereoelectroencephalography (SEEG) in patients with medi...

Revisiting Video Saliency Prediction in the Deep Learning Era.

Predicting where people look in static scenes, a.k.a visual saliency, has received significant research interest recently. However, relatively less ef...

UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification.

Deep learning has achieved remarkable success in the optical coherence tomography (OCT) image classification task with substantial labelled B-scan ima...

Dehaze of Cataractous Retinal Images Using an Unpaired Generative Adversarial Network.

Cataracts are the leading cause of visual impairment worldwide. Examination of the retina through cataracts using a fundus camera is challenging and e...

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...

MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images.

As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one ...

End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging.

Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but inter-individual treatment require...

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...