Ophthalmology

An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images.

Diabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for monitoring their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automa...

Artificial Intelligence-Assisted Early Detection of Retinitis Pigmentosa - the Most Common Inherited Retinal Degeneration.

The purpose of this study was to detect the presence of retinitis pigmentosa (RP) based on color fundus photographs using a deep learning model. A total of 1670 color fundus photographs from the Taiwan inherited retinal degeneration project and National Taiwan University Hospital were acquired and preprocessed. The fundus photographs were labeled RP or normal and divided into training and validati...

I Understand You: Blind 3D Human Attention Inference From the Perspective of Third-Person.

Inferring object-wise human attention in 3D space from the third-person perspective (e.g., a camera) is crucial to many visual tasks and applications,...

Automatic detection of papilledema through fundus retinal images using deep learning.

Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities ...

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning.

Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, wit...

Self-Supervised Feature Learning and Phenotyping for Assessing Age-Related Macular Degeneration Using Retinal Fundus Images.

OBJECTIVE: Diseases such as age-related macular degeneration (AMD) are classified based on human rubrics that are prone to bias. Supervised neural net...

Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs.

OBJECTIVES: To present and validate a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fund...

A Guide to Accessible Artificial Intelligence and Machine Learning for the 21st Century Retina Specialist.

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach.

Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about introducing "disruptive" technologies persists. Intri...

Photonic Crystal Fiber-Based Reconfigurable Biosensor Using Phase Change Material.

A reconfigurable biosensor with different spectral sensitivities could provide new opportunities to increase the label-free selectivity and sensitivit...

Two degree-of-freedom robotic eye: design, modeling, and learning-based control in foveation and smooth pursuit.

With increasing ocular motility disorders affecting human eye movement, the need to understand the biomechanics of the human eye rises constantly. A r...

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment ...

Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration.

PURPOSE: This study aimed to determine the efficacy of a multimodal deep learning (DL) model using optical coherence tomography (OCT) and optical cohe...

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly...

AI-based monitoring of retinal fluid in disease activity and under therapy.

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomo...

Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.

INTRODUCTION: Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In...

A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information.

Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, t...

Adversarial attack vulnerability of medical image analysis systems: Unexplored factors.

Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have rec...

Preventing corneal blindness caused by keratitis using artificial intelligence.

Keratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. T...

Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression.

OBJECTIVE: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration...