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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2020
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| Materias: | |
| 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 |