Nicholas Riina, Alon Harris, Brent A Siesky, Lukas Ritzer, Louis R Pasquale, James C Tsai, James Keller, Barbara Wirostko, Julia Arciero, Brendan Fry, George Eckert, Alice Verticchio Vercellin, Gal Antman, Paul A Sidoti, Giovanna Guidoboni
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...
September 3, 2024: Investigative Ophthalmology & Visual Science