Invest Ophthalmol Vis Sci
September 2024
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).
Methods: Neural network models, also known as multi-layer perceptrons (MLPs), were trained on a prospectively collected observational dataset comprised of 93 glaucoma patients confirmed by a glaucoma specialist and 113 control subjects. The base model used only intraocular pressure, blood pressure, heart rate, and visual field (VF) parameters to diagnose glaucoma.
: To investigate macular vascular biomarkers for the detection of primary open-angle glaucoma (POAG). : A total of 56 POAG patients and 94 non-glaucomatous controls underwent optical coherence tomography angiography (OCTA) assessment of macular vessel density (VD) in the superficial (SCP), and deep (DCP) capillary plexus, foveal avascular zone (FAZ) area, perimeter, VD, choriocapillaris and outer retina flow area. POAG patients were classified for severity based on the Glaucoma Staging System 2 of Brusini.
View Article and Find Full Text PDFPurpose: The purpose of this study was to investigate the effects of artificial tears (AT) on the sublayers of the tear film assessed by a novel tear film imaging (TFI) device.
Methods: The mucoaqueous layer thickness (MALT) and lipid layer thickness (LLT) of 198 images from 11 healthy participants, 9 of whom had meibomian gland disease, were prospectively measured before and after exposure to 3 different AT preparations (Refresh Plus; Retaine [RTA]; Systane Complete PF [SYS]), using a novel nanometer resolution TFI device (AdOM, Israel). Participants were assessed at baseline and at 1, 5, 10, 30, and 60 minutes after instilling 1 drop of AT during 3 sessions on separate days.