Using Artificial Intelligence and Novel Polynomials to Predict Subjective Refraction

This work aimed to use artificial intelligence to predict subjective refraction from wavefront aberrometry data processed with a novel polynomial decomposition basis. Subjective refraction was converted to power vectors (M, J0, J45). Three gradient boosted trees (XGBoost) algorithms were trained to...

Descripción completa

Detalles Bibliográficos
Autores principales: Rampat, Radhika, Debellemanière, Guillaume, Malet, Jacques, Gatinel, Damien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7244728/
https://www.ncbi.nlm.nih.gov/pubmed/32444650
http://dx.doi.org/10.1038/s41598-020-65417-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7244728/
https://www.ncbi.nlm.nih.gov/pubmed/32444650
http://dx.doi.org/10.1038/s41598-020-65417-y

Ejemplares similares