Ophthalmology

A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography.

As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinopathy (DR). Remarkable advances in technology have made it possible to use artificial intelligence to screen DR from retinal images with high accuracy and reliability, resulting in reducing human labor by processing large amounts of data in a shorter time. We developed a fully automated classificatio...

Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.

As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking neural networks. In this paper, we present a supervised learning algorithm for multilayer feedforward spiking neural networks; all neurons can fire mul...

Human Oocyte Morphology and Outcomes of Infertility Treatment: a Systematic Review.

Oocyte morphology assessment is easy to implement in any laboratory with possible quality grading prior to fertilization. At present, comprehensive oo...

Methodological advances in the discovery of novel neuroblastoma therapeutics.

INTRODUCTION: Neuroblastoma is a cancer of the sympathetic nervous system that causes up to 15% of cancer-related deaths among children. Among the ~1,...

Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.

Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to det...

Free Space Detection Using Camera-LiDAR Fusion in a Bird's Eye View Plane.

Although numerous road segmentation studies have utilized vision data, obtaining robust classification is still challenging due to vision sensor noise...

Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images.

The purpose of this study was to develop an automatic deep learning-based approach and corresponding free, open-source software to perform segmentatio...

Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms.

Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progre...

Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique.

INTRODUCTION: Early detection of visual symptoms in pterygium patients is crucial as the progression of the disease can cause visual disruption and co...

First-in-Human Robot-Assisted Subretinal Drug Delivery Under Local Anesthesia.

PURPOSE: To report the results of a first-in-human study using a robotic device to assist subretinal drug delivery in patients undergoing vitreoretina...

Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes.

PURPOSE: To compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of ins...

StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model.

To measure stomatal traits automatically and nondestructively, a new method for detecting stomata and extracting stomatal traits was proposed. Two por...

Study on augmented reality for robotic surgery bedside assistants.

Robotic surgery bedside assistants play an important role in robotic procedures by performing intra-corporeal tasks while accommodating the physical p...

Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.

Cellularity estimation forms an important aspect of the visual examination of bone marrow biopsies. In clinical practice, cellularity is estimated by ...

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature.

BACKGROUND: The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal ...

General deep learning model for detecting diabetic retinopathy.

BACKGROUND: Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency...

Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks.

BACKGROUND: The prevalence of chronic disease is growing in aging societies, and artificial-intelligence-assisted interpretation of macular degenerati...

Deepfake Detection Using the Rate of Change between Frames Based on Computer Vision.

Recently, artificial intelligence has been successfully used in fields, such as computer vision, voice, and big data analysis. However, various proble...

A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection.

Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection...

Autonomous Thermal Vision Robotic System for Victims Recognition in Search and Rescue Missions.

Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to ...