Ophthalmology

Latest AI and machine learning research in ophthalmology for healthcare professionals.

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Can off-the-shelf visual large language models detect and diagnose ocular diseases from retinal photographs?

BACKGROUND: The advent of generative artificial intelligence has led to the emergence of multiple vi...

Hybrid vision GNNs based early detection and protection against pest diseases in coffee plants.

Agriculture is an essential foundation that supports numerous economies, and the longevity of the co...

Emittance minimization for aberration correction I: Aberration correction of an electron microscope without knowing the aberration coefficients.

Precise alignment of the electron beam is critical for successful application of scanning transmissi...

Retinal OCT image segmentation with deep learning: A review of advances, datasets, and evaluation metrics.

Optical coherence tomography (OCT) is a widely used imaging technology in ophthalmic clinical practi...

Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee.

Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with di...

[Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].

Imaging disciplines, such as ophthalmology, offer a wide range of opportunities for the beneficial u...

Computer vision and tactile glove: A multimodal model in lifting task risk assessment.

Work-related injuries from overexertion, particularly lifting, are a major concern in occupational s...

AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings.

IMPORTANCE: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness...

Comparative analysis of deep learning architectures for thyroid eye disease detection using facial photographs.

PURPOSE: To compare two artificial intelligence (AI) models, residual neural networks ResNet-50 and ...

Chat GPT vs an experienced ophthalmologist: evaluating chatbot writing performance in ophthalmology.

PURPOSE: To examine the abilities of ChatGPT in writing scientific ophthalmology introductions and t...

Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis.

The worldwide prevalence of glaucoma makes it a major reason for blindness thus proper early diagnos...

Triage of Patient Messages Sent to the Eye Clinic via the Electronic Medical Record: A Comparative Study on AI and Human Triage Performance.

: Assess the ability of ChatGPT-4 (GPT-4) to effectively triage patient messages sent to the general...

LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images.

Deep learning methods have significantly advanced the field of gaze estimation, yet the development ...

VisionGuard: enhancing diabetic retinopathy detection with hybrid deep learning.

OBJECTIVES: Early detection of diabetic retinopathy (DR) and timely intervention are critical for pr...

Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset...

Artificial intelligence applied to electroencephalography in epilepsy.

Artificial intelligence (AI) is progressively transforming all fields of medicine, promising substan...

Analyzing resuscitation conference content through the lens of the chain of survival.

BACKGROUND: Resuscitation science today often focuses on advanced topics such as extracorporeal card...

A vision attention driven Language framework for medical report generation.

This study introduces the Medical Vision Attention Generation (MedVAG) model, a novel framework desi...

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