A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps

PURPOSE: To develop and assess the accuracy of a hybrid deep learning construct for detecting keratoconus (KCN) based on corneal topographic maps. METHODS: We collected 3794 corneal images from 542 eyes of 280 subjects and developed seven deep learning models based on anterior and posterior eccentri...

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Detalles Bibliográficos
Autores principales: Al-Timemy, Ali H., Mosa, Zahraa M., Alyasseri, Zaid, Lavric, Alexandru, Lui, Marcelo M., Hazarbassanov, Rossen M., Yousefi, Siamak
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684312/
https://www.ncbi.nlm.nih.gov/pubmed/34913952
http://dx.doi.org/10.1167/tvst.10.14.16

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8684312/
https://www.ncbi.nlm.nih.gov/pubmed/34913952
http://dx.doi.org/10.1167/tvst.10.14.16

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