Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma

• Published in: Investigative ophthalmology & visual science Vol. 65; no. 11; p. 16
• Main Authors: Riina, Nicholas, Harris, Alon, Siesky, Brent A., Ritzer, Lukas, Pasquale, Louis R., Tsai, James C., Keller, James, Wirostko, Barbara, Arciero, Julia, Fry, Brendan, Eckert, George, Verticchio Vercellin, Alice, Antman, Gal, Sidoti, Paul A., Guidoboni, Giovanna
• Format: Journal Article
• Language: English
• Published: United States The Association for Research in Vision and Ophthalmology 09.09.2024
• Subjects: Aged; Area Under Curve; Biomarkers; Female; Glaucoma; Glaucoma, Open-Angle - diagnosis; Glaucoma, Open-Angle - physiopathology; Humans; Intraocular Pressure - physiology; Male; Middle Aged; Nerve Fibers - pathology; Neural Networks, Computer; Optic Disk - diagnostic imaging; Optic Disk - pathology; Prospective Studies; Retinal Ganglion Cells - pathology; ROC Curve; Tomography, Optical Coherence - methods; Visual Fields - physiology

To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG). Neural network models, also known as multi-layer perceptrons (MLPs), were trained on a prospectively collected observational dataset comprised o...