Glaucoma

Assessing the Potential of AI-Driven Drug Repurposing in Ophthalmology: An Analysis of ChatGPT’s Therapeutic Recommendations

This study aimed to evaluate the novelty and potential value of therapeutic suggestions made by an artificial intelligence large language model for treating various ophthalmic diseases. ChatGPT-3.5 was used to suggest novel ophthalmic indications for available medications. The generation of therapeutic suggestions was performed by inputting standardized queries about treatments for common ophthalm...

GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment

Purpose: Accurate glaucoma assessment is challenging because of the complexity and chronic nature of the disease; therefore, there is a critical need for models that provide evidence-based, accurate assessment. The purpose of this study was to evaluate the capabilities of a glaucoma specialized Retrieval-Augmented Generation (RAG) framework (GlaucoRAG) that leverages a large language model (LLM) f...

AI-GUIDED ENDPOINT SELECTION FOR NEUROPROTECTION TRIALS IN GLAUCOMA

Standard Automated Perimetry (SAP) is the mainstay for monitoring glaucoma progression and has been accepted by the U.S. Food and Drug Administration ...

Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus Photographs

To compare the performance of a foundation model and a supervised learning-based model for detecting referable glaucoma from fundus photographs. Evalu...

Clinically Informed Semi-Supervised Learning Improves Disease Annotation and Equity from Electronic Health Records: A Glaucoma Case Study

Clinical notes represent a vast but underutilized source of information for disease characterization, whereas structured electronic health record (EHR...

Artificial Intelligence Powered Research Automation (AIPRA) Versus Human Expert: A Two-Arm Ophthalmology Comparative Study

To compare the quality and efficiency of an AI-powered research automation (AIPRA) workflow with a conventional human-led workflow for producing a ful...

Explainable Deep Learning for Glaucoma Detection: A DenseNet121-Based Classification with Grad-CAM Visualization

One of the main causes of permanent blindness in the globe, glaucoma frequently advances symptomlessly until it reaches an advanced stage. Recent deve...

Multimodal Prediction of Primary Open-Angle Glaucoma Using Polygenic Risk Scores and Clinical Features in a High-Risk African Ancestry Cohort

Primary open-angle glaucoma (POAG) disproportionately affects individuals of African ancestry, yet early detection tools remain limited. Using the lar...

Multimodal Deep Learning for Longitudinal Prediction of Glaucoma Progression Using Sequential RNFL, Visual Field, and Clinical Data

Forecasting glaucoma progression remains a major challenge in preventing irreversible vision loss. We developed and validated a multimodal, longitudin...

Deep-learning-derived glaucoma-related endophenotypes enable novel genome-wide genetic and functional discovery

The genetic architecture of primary open-angle glaucoma (POAG), a leading cause of irreversible blindness, remains largely unexplained due to the reli...

Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes using a Local Large Language Model

To evaluate the performance of a large language model (LLM) in identifying medication non-adherence, visit non-adherence, and family history of glauco...

A Federated Learning-based Optic Disc and Cup Segmentation Model for Glaucoma Monitoring In Color Fundus Photographs

Glaucoma, a leading cause of blindness worldwide, depends on accurate optic nerve head assessment, particularly optic disc and cup segmentation, for d...

Glaucoma Detection Using Deep Learning and Prompt-Based Explainable Report Generation

Glaucoma is a leading cause of irreversible blindness and requires early detection to prevent vision loss. This study proposes a novel framework for a...

Leveraging molecular-QTL co-association to predict novel disease-associated genetic loci using a graph convolutional neural network.

Genome-wide association studies (GWAS) have successfully uncovered numerous associations between genetic variants and disease traits to date. Yet, ide...

[Primary angle closure suspects: application of machine learning method for substantiation of close monitoring].

UNLABELLED: One of the priority areas in healthcare is the concept of predictive, preventive and personalized medicine, which is based on an individua...

DiffuPT: Class Imbalance Mitigation for Glaucoma Detection via Diffusion Based Generation and Model Pretraining

Glaucoma is a progressive optic neuropathy characterized by structural damage to the optic nerve head and functional changes in the visual field. De...

Identification and validation of oxidative stress-related genes in primary open-angle glaucoma by weighted gene co-expression network analysis and machine learning.

Primary open-angle glaucoma (POAG) is a common ocular disease, and there is currently no effective treatment for POAG therapy. Thus, identifying some ...

A Computational Fluid Dynamics study of drug-releasing ocular implants for glaucoma treatment: Comparison of implant size and locations

Drug-releasing implants are gaining momentum in the treatment of glaucoma. Implants present however several limitations. Among these limitations, th...

Beyond PhacoTrainer: Deep Learning for Enhanced Trabecular Meshwork Detection in MIGS Videos.

PURPOSE: The purpose of this study was to develop deep learning models for surgical video analysis, capable of identifying minimally invasive glaucoma...

Long-Term Rate of Optic Disc Rim Loss in Glaucoma Patients Measured From Optic Disc Photographs With a Deep Neural Network.

PURPOSE: This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements and the disc damage likelihood scale (DDLS) to meas...