SIGNIFICANCE: Glaucoma, a leading cause of global blindness, disproportionately affects low-income regions due to expensive diagnostic methods. Affordable intraocular pressure (IOP) measurement is crucial for early detection, especially in low- and m...
PRCIS: Machine learning classifiers are an effective approach to detecting glaucomatous fundus images based on optic disc topographic features making it a straightforward and effective approach.
PRCIS: In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6...
PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.
PURPOSE: To develop machine learning (ML) and deep learning (DL) models to predict glaucoma surgical outcomes, including postoperative intraocular pressure, use of ocular antihypertensive medications, and need for repeat surgery.
BACKGROUND: Ahmed valve implantation demonstrated an increasing proportion in glaucoma surgery, but predicting the successful maintenance of target intraocular pressure remains a challenging task. This study aimed to evaluate the performance of machi...
PRCIS: We developed unsupervised machine learning models to identify different subtypes of patients with ocular hypertension in terms of visual field (VF) progression and discovered 4 subtypes with different trends of VF worsening. We then identified...
PURPOSE: To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG).
PURPOSE: This study uses deep neural network-generated rim-to-disc area ratio (RADAR) measurements and the disc damage likelihood scale (DDLS) to measure the rate of optic disc rim loss in a large cohort of glaucoma patients.
BACKGROUND/AIMS: To design a deep learning (DL) model for the detection of glaucoma progression with a longitudinal series of macular optical coherence tomography angiography (OCTA) images.