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Prediction of manifest refraction using machine learning ensemble models on wavefront aberrometry data

Crossref DOI link: https://doi.org/10.1016/j.optom.2022.03.001

Published Print: 2022

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Hernández, Carlos S.

Gil, Andrea

Casares, Ignacio

Poderoso, Jesús https://orcid.org/0000-0003-4020-1563
Wehse, Alec https://orcid.org/0000-0001-9307-1550
Dave, Shivang R. https://orcid.org/0000-0001-9568-4289
Lim, Daryl

Sánchez-Montañés, Manuel

Lage, Eduardo https://orcid.org/0000-0003-0011-2451
Funding

Funding for this research was provided by:

Ministerio de Economía y Competitividad (RYC-2016-21125)

National Institutes of Health

Agencia Estatal de Investigación (PGC2018- 095895-B-I00)

National Eye Institute (R44EY025452)

National Institute of Biomedical Imaging and Bioengineering (R43EB024299)

Comunidad de Madrid (IND2019/TIC-17116, IND2020/TIC-17340, PEJD-2019-PRE/IND-15478, S2017/BMD-3688)

Federación Española de Enfermedades Raras
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Text and Data Mining valid from 2022-01-01

Text and Data Mining valid from 2022-01-01

Version of Record valid from 2022-03-08
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This article is maintained by: Elsevier

Article Title: Prediction of manifest refraction using machine learning ensemble models on wavefront aberrometry data

Journal Title: Journal of Optometry

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.optom.2022.03.001

Content Type: article

Copyright: © 2022 Spanish General Council of Optometry. Published by Elsevier España, S.L.U.