Glaucoma

Vision-Language Models vs Autonomous AI Agents for Anterior Capsular Radial Folds: A Diagnostic Study

ImportanceVision-language models (VLMs) enable generalist multimodal reasoning, but their ability to resolve brief, low-contrast cues in surgical video without task-specific training is uncertain. Autonomous artificial intelligence (AI) agents offer an alternative paradigm by autonomously generating supervised classifiers tailored to specific visual tasks. ObjectiveTo benchmark the performance of...

F^2TTA: Free-Form Test-Time Adaptation on Cross-Domain Medical Image Classification via Image-Level Disentangled Prompt Tuning

Test-Time Adaptation (TTA) has emerged as a promising solution for adapting a source model to unseen medical sites using unlabeled test data, due to the high cost of data annotation. Existing TTA methods consider scenarios where data from one or multiple domains arrives in complete domain units. However, in clinical practice, data usually arrives in domain fragments of arbitrary lengths and in r...

Evaluating Large Language Models for Multimodal Simulated Ophthalmic Decision-Making in Diabetic Retinopathy and Glaucoma Screening

Large language models (LLMs) can simulate clinical reasoning based on natural language prompts, but their utility in ophthalmology is largely unexpl...

Early Glaucoma Detection using Deep Learning with Multiple Datasets of Fundus Images

Glaucoma is a leading cause of irreversible blindness, but early detection can significantly improve treatment outcomes. Traditional diagnostic meth...

MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models

The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologi...

Robust Uncertainty-Informed Glaucoma Classification Under Data Shift.

PURPOSE: Standard deep learning (DL) models often suffer significant performance degradation on out-of-distribution (OOD) data, where test data differ...

Eye-Tracking Algorithm for Early Glaucoma Detection: Analysis of Saccadic Eye Movements in Primary Open-Angle Glaucoma.

Glaucoma remains a leading cause of irreversible blindness worldwide, with early detection crucial for preventing vision loss. This study developed an...

An Attention Infused Deep Learning System with Grad-CAM Visualization for Early Screening of Glaucoma

This research work reveals the eye opening wisdom of the hybrid labyrinthine deep learning models synergy born out of combining a trailblazing convo...

A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis.

Glaucoma is a group of serious eye diseases that can cause incurable blindness. Despite the critical need for early detection, over 60% of cases remai...

RetinaLogos: Fine-Grained Synthesis of High-Resolution Retinal Images Through Captions

The scarcity of high-quality, labelled retinal imaging data, which presents a significant challenge in the development of machine learning models fo...

Accelerated Biological Aging in Exfoliation Glaucoma Assessed by Fundus-Derived Predicted Age and Advanced Glycation End Products.

Glaucoma is an age-related neurodegenerative disease characterized by progressive optic nerve damage. Accelerated biological aging, assessed using pre...

Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model.

Glaucoma is a leading cause of irreversible blindness worldwide; therefore, detection of this disease in its early stage is crucial. However, previous...

Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges.

Deep learning algorithms as tools for automated image classification have recently experienced rapid growth in imaging-dependent medical specialties, ...

Ethics of Artificial Intelligence in Medicine and Ophthalmology.

BACKGROUND: This review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first i...

Telemonitoring Tools for Glaucoma Patients: A Systematic Review of Current Trends and Applications.

: In 2010, approximately 60.5 million people were affected by glaucoma, making it the leading cause of permanent vision impairment globally. With the ...

Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.

Glaucoma is a leading cause of irreversible blindness, with challenges persisting in early diagnosis, disease progression, and surgical outcome predic...

High-Accuracy Digitization of Humphrey Visual Field Reports Using Convolutional Neural Networks.

PURPOSE: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise visual field (VF) assessments for effective diagnosis ...

Opening the black box of deep learning: Validating the statistical association between explainable artificial intelligence (XAI) and clinical domain knowledge in fundus image-based glaucoma diagnosis

While deep learning has exhibited remarkable predictive capabilities in various medical image tasks, its inherent black-box nature has hindered its ...

Enhancing Fundus Image-based Glaucoma Screening via Dynamic Global-Local Feature Integration

With the advancements in medical artificial intelligence (AI), fundus image classifiers are increasingly being applied to assist in ophthalmic diagn...

3D Structural Phenotype of the Optic Nerve Head at the Intersection of Glaucoma and Myopia -- A Key to Improving Glaucoma Diagnosis in Myopic Populations

Purpose: To characterize the 3D structural phenotypes of the optic nerve head (ONH) in patients with glaucoma, high myopia, and concurrent high myop...