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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...
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| Published in: | Sci Rep |
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| Main Authors: | , , , |
| Format: | Artigo |
| Language: | Inglês |
| Published: |
Nature Publishing Group UK
2020
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| Subjects: | |
| Online Access: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7244728/ https://ncbi.nlm.nih.gov/pubmed/32444650 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-020-65417-y |
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