Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms

PURPOSE: To develop a machine learning regression model of subjective refractive prescription from minimum ocular biometry and corneal topography features. METHODS: Anterior corneal surface parameters (Zernike coefficients and keratometry), axial length, anterior chamber depth, and age were posed as...

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Detalles Bibliográficos
Autores principales: Espinosa, Julián, Pérez, Jorge, Villanueva, Asier
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/PMC9034724/
https://www.ncbi.nlm.nih.gov/pubmed/35404439
http://dx.doi.org/10.1167/tvst.11.4.8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9034724/
https://www.ncbi.nlm.nih.gov/pubmed/35404439
http://dx.doi.org/10.1167/tvst.11.4.8

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