Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning

PURPOSE: To assess the performance of a perimetric strategy using structure–function predictions from a deep learning (DL) model. METHODS: Visual field test–retest data from 146 eyes (75 patients) with glaucoma with (median [5th–95th percentile]) 10 [7, 10] tests per eye were used. Structure–functio...

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Detalles Bibliográficos
Autores principales: Montesano, Giovanni, Lazaridis, Georgios, Ometto, Giovanni, Crabb, David P., Garway-Heath, David F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2023
Materias:
Glaucoma
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587851/
https://www.ncbi.nlm.nih.gov/pubmed/37831447
http://dx.doi.org/10.1167/tvst.12.10.10

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10587851/
https://www.ncbi.nlm.nih.gov/pubmed/37831447
http://dx.doi.org/10.1167/tvst.12.10.10

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