Vahid Mohammadzadeh, Sean Wu, Sajad Besharati, Tyler Davis, Arvind Vepa, Esteban Morales, Kiumars Edalati, Mahshad Rafiee, Arthur Martinyan, David Zhang, Fabien Scalzo, Joseph Caprioli, Kouros Nouri-Mahdavi
PURPOSE: Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL). METHODS: SETTING: Tertiary academic center. DESIGN: Development of a DL algorithm to predict VF progression. STUDY POPULATION: 3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up...
February 12, 2024: American Journal of Ophthalmology