Machine-learning random forest algorithms predict post-cycloplegic myopic corrections from noncycloplegic clinical data.
Journal:
Optometry and vision science : official publication of the American Academy of Optometry
PMID:
39993303
Abstract
SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncycloplegic clinical data. A classification model predicted post-cycloplegic myopia and could be useful in screening, and a second regression model predicted post-cycloplegic refractive and could provide a useful objective starting point in noncycloplegic subjective refractions.