An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis

PURPOSE: To detect visual field (VF) progression by analyzing spatial pattern changes. METHODS: We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously de...

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Detalles Bibliográficos
Autores principales: Wang, Mengyu, Shen, Lucy Q., Pasquale, Louis R., Petrakos, Paul, Formica, Sydney, Boland, Michael V., Wellik, Sarah R., De Moraes, Carlos Gustavo, Myers, Jonathan S., Saeedi, Osamah, Wang, Hui, Baniasadi, Neda, Li, Dian, Tichelaar, Jorryt, Bex, Peter J., Elze, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2019
Materias:
Glaucoma
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348996/
https://www.ncbi.nlm.nih.gov/pubmed/30682206
http://dx.doi.org/10.1167/iovs.18-25568

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6348996/
https://www.ncbi.nlm.nih.gov/pubmed/30682206
http://dx.doi.org/10.1167/iovs.18-25568

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