Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets

PURPOSE: To develop a three-dimensional (3D) deep learning algorithm to detect glaucoma using spectral-domain optical coherence tomography (SD-OCT) optic nerve head (ONH) cube scans and validate its performance on ethnically diverse real-world datasets and on cropped ONH scans. METHODS: In total, 24...

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Detalles Bibliográficos
Autores principales: Noury, Erfan, Mannil, Suria S., Chang, Robert T., Ran, An Ran, Cheung, Carol Y., Thapa, Suman S., Rao, Harsha L., Dasari, Srilakshmi, Riyazuddin, Mohammed, Chang, Dolly, Nagaraj, Sriharsha, Tham, Clement C., Zadeh, Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145034/
https://www.ncbi.nlm.nih.gov/pubmed/35551345
http://dx.doi.org/10.1167/tvst.11.5.11

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145034/
https://www.ncbi.nlm.nih.gov/pubmed/35551345
http://dx.doi.org/10.1167/tvst.11.5.11

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