Ophthalmology

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 vision large language models (VLLMs). This study aimed to evaluate the capabilities of commonly available VLLMs, such as OpenAI's GPT-4V and Google's Gemini, in detecting and diagnosing ocular diseases from retinal images.

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 coffee business is of paramount significance. Controlling and safeguarding coffee farms from harmful pests, including the Coffee Berry Borer, Mealybugs, Scales, and Leaf Miners, which may drastically affect crop productivity and quality. Standard methods for detecting pest diseases sometimes need spec...

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 transmission electron microscopes (STEM) to understanding ma...

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 practice, providing non-invasive access to high-resoluti...

5-5-5 ABRT (Dose of 5 Gy per Fraction for up to 5 Fractions Over 5 Weeks Adaptive Bridging Radiation Therapy)-Artificial Intelligence Enters the CAR (-T) (Chimeric Antigen Receptor-T) in Relapsed/Refractory Large B Cell Lymphoma.

PURPOSE: Bridging radiation therapy (BRT) is effective for local control in patients with relapsed or refractory large B cell lymphoma who are undergo...

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 diabetes, because of early detection and treatment o...

[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 use of artificial intelligence (AI). The analysis o...

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 ResNet-101, for screening thyroid eye disease (TED...

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

IMPORTANCE: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. If detected and treated early, ROP-ass...

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 diagnosis remains essential for preventing major vision d...

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 of these algorithms is often hindered by a lack of...

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 eye clinic at our institution. : Patient messages...

VisionGuard: enhancing diabetic retinopathy detection with hybrid deep learning.

OBJECTIVES: Early detection of diabetic retinopathy (DR) and timely intervention are critical for preventing vision loss. Recently, deep learning tech...

Artificial intelligence applied to electroencephalography in epilepsy.

Artificial intelligence (AI) is progressively transforming all fields of medicine, promising substantial changes in clinical practice. In the context ...

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

BACKGROUND: Resuscitation science today often focuses on advanced topics such as extracorporeal cardiopulmonary resuscitation or targeted temperature ...

A vision attention driven Language framework for medical report generation.

This study introduces the Medical Vision Attention Generation (MedVAG) model, a novel framework designed to facilitate the automated generation of med...

Hybrid deep learning framework for diabetic retinopathy classification with optimized attention AlexNet.

Diabetic retinopathy (DR) is a chronic condition associated with diabetes that can lead to vision impairment and, if not addressed, may progress to ir...

Evaluating multimodal ChatGPT for emergency decision-making of ocular trauma cases.

PURPOSE: This study aimed to evaluate the potential of ChatGPT in diagnosing ocular trauma cases in emergency settings and determining the necessity f...

Sensor-driven digital motion correction of robotically-aligned optical coherence tomography retinal volumes.

Optical coherence tomography (OCT) has revolutionized diagnostics in retinal ophthalmology. Traditional OCT requires minimal relative motion between t...

Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.

Continuous high-resolution imaging of the disease-mediating blood stages of the human malaria parasite Plasmodium falciparum faces challenges due to p...