Latest AI and machine learning research in glaucoma for healthcare professionals.
BACKGROUND: Robot-assisted laparoscopic radical prostatectomy (RALP) is a minimally invasive surgical procedure for prostate cancer. During RALP, the patient must be in a steep Trendelenburg (head-down) position, which leads to a significant increase in intraocular pressure (IOP). The association of RALP with visual field sensitivity, however, has not been prospectively studied. The purpose of thi...
Background: Artificial intelligence (AI) systems for glaucoma diagnosis and prognostication from visual fields (VF) are under active development, yet do not audit for vertical-meridian-respecting field loss - known sequelae of stroke, hemorrhage, and neoplasm. We developed a self-supervised encoder of automated perimetry that learns anatomically interpretable VF structure without labels, and evalu...
Precise segmentation of the optic disc and cup is critical for the early detection and diagnosis of glaucoma. However, achieving consistently high per...
Accurate localization and segmentation of the optic disc (OD) are important for retinal image analysis and glaucoma assessment, yet remain challenging...
The population structure of an inbred population of 781 people on Norfolk Island in the Pacific, 318 of which are descendants of the original Mutineer...
Glaucoma is a group of eye diseases that damage the optic nerve, often caused by elevated intraocular pressure. It is a leading cause of irreversible ...
Frozen encoders are chosen by how well a lightweight head reads a finding from their features, not whether the geometry separates it. Nearest-neighbor...
Background: Retinal fundus imaging is central to the early diagnosis of sight-threatening conditions including diabetic retinopathy, glaucoma, and ret...
Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but exist...
Glaucoma is a leading cause of irreversible blindness worldwide, yet most automated diagnosis systems rely on opaque deep-learning models that offer l...
Forecasting visual fields (VFs) is critical for personalized monitoring and treatment planning in glaucoma. This is inherently uncertain due to hetero...
Automated glaucoma subtype classification from clinical notes remains clinically unactionable without subspecialty-aligned explanations supporting cli...
Purpose: To determine whether disease-aware adversarial perturbations can reduce demographic recoverability encoded in color fundus photographs (CFPs)...
Purpose: To evaluate the performance of secure cloud-based large language models (LLMs) in extracting glaucoma diagnosis, type, and severity from free...
Purpose: To evaluate the efficacy of large language models (LLMs) in extracting medication-related information from glaucoma clinical notes in the ele...
This work proposes an integrated pipeline for automatic glaucoma detection method from easily available colour fundas images based on an adaptive algo...
Glaucoma is a leading cause of irreversible blindness worldwide, and early detection from fundus images is critical for effective disease management. ...
Background/Aims: Neovascular glaucoma (NVG) is an aggressive secondary glaucoma with limited longitudinal data. This study reports the aetiologies, tr...
Purpose To investigate the relative efficacy of nine distinct visual field (VF) denoising artificial intelligence (AI) methods and a pathology-aware A...
Purpose: To develop and evaluate a deep learning model that predicts optical coherence tomography (OCT)-equivalent retinal nerve fiber layer thickness...