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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Veröffentlicht in:	Scientific reports 2020-05, Vol.10 (1), p.8565, Article 8565
Hauptverfasser:	Rampat, Radhika, Debellemanière, Guillaume, Malet, Jacques, Gatinel, Damien
Format:	Artikel
Sprache:	eng
Schlagworte:	631/114/1305 639/624/1107/510 Algorithms Artificial intelligence Decomposition Humanities and Social Sciences Learning algorithms Machine learning multidisciplinary Refraction Science Science (multidisciplinary)
Online-Zugang:	Volltext
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