Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma

PURPOSE: Correcting retinal nerve fiber layer thickness (RNFLT) artifacts in glaucoma with deep learning and evaluate its clinical usefulness. METHODS: We included 24,257 patients with optical coherence tomography and reliable visual field (VF) measurements within 30 days and 3,233 patients with rel...

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Detalles Bibliográficos
Autores principales: Shi, Min, Sun, Jessica A., Lokhande, Anagha, Tian, Yu, Luo, Yan, Elze, Tobias, Shen, Lucy Q., Wang, Mengyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2023
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631515/
https://www.ncbi.nlm.nih.gov/pubmed/37934137
http://dx.doi.org/10.1167/tvst.12.11.12

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631515/
https://www.ncbi.nlm.nih.gov/pubmed/37934137
http://dx.doi.org/10.1167/tvst.12.11.12

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