Latest AI and machine learning research in glaucoma for healthcare professionals.
Accurate retinal Microvascular segmentation demands a balanced combination of anatomical fidelity and hemodynamic relevance. However, existing methods fall short in preserving critical structures such as capillary junctions and bifurcations, thus limiting clinical applications and causing fragmentation. To address these limitations, we propose DFMS-Net, a novel dualfield segmentation framework tha...
To develop an equitable deep learning model with knowledge distillation to enhance the demographic equity in glaucoma progression prediction. We developed a novel deep learning model called FairDist which used baseline optical coherency tomography (OCT) scans to predict glaucoma progression. First, an equity-aware EfficientNet termed EqEffNet was trained for glaucoma detection. Next, the pretraine...
This study evaluates the diagnostic performance of several AI models, including Deepseek, in diagnosing corneal diseases, glaucoma, and neuro□ophthalm...
Glaucoma is a leading cause of irreversible blindness worldwide, with early diagnosis often hindered by subtle symptomatology and the lack of comprehe...
Real-world ocular imaging datasets are essential for advancing research in artificial intelligence (AI), autonomous disease screening, and clinical de...
Glaucoma is increasingly recognized as a neurodegenerative condition involving both retinal and central nervous system structures. Here, we present an...
This study aimed to evaluate the novelty and potential value of therapeutic suggestions made by an artificial intelligence large language model for tr...
Purpose: Accurate glaucoma assessment is challenging because of the complexity and chronic nature of the disease; therefore, there is a critical need ...
Standard Automated Perimetry (SAP) is the mainstay for monitoring glaucoma progression and has been accepted by the U.S. Food and Drug Administration ...
To compare the performance of a foundation model and a supervised learning-based model for detecting referable glaucoma from fundus photographs. Evalu...
Clinical notes represent a vast but underutilized source of information for disease characterization, whereas structured electronic health record (EHR...
To compare the quality and efficiency of an AI-powered research automation (AIPRA) workflow with a conventional human-led workflow for producing a ful...
One of the main causes of permanent blindness in the globe, glaucoma frequently advances symptomlessly until it reaches an advanced stage. Recent deve...
Primary open-angle glaucoma (POAG) disproportionately affects individuals of African ancestry, yet early detection tools remain limited. Using the lar...
Forecasting glaucoma progression remains a major challenge in preventing irreversible vision loss. We developed and validated a multimodal, longitudin...
The genetic architecture of primary open-angle glaucoma (POAG), a leading cause of irreversible blindness, remains largely unexplained due to the reli...
To evaluate the performance of a large language model (LLM) in identifying medication non-adherence, visit non-adherence, and family history of glauco...
Glaucoma, a leading cause of blindness worldwide, depends on accurate optic nerve head assessment, particularly optic disc and cup segmentation, for d...
Glaucoma is a leading cause of irreversible blindness and requires early detection to prevent vision loss. This study proposes a novel framework for a...
Genome-wide association studies (GWAS) have successfully uncovered numerous associations between genetic variants and disease traits to date. Yet, ide...