Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length.

Journal: Translational vision science & technology

PMID: 38809529

Abstract

PURPOSE: The purpose of this study was to investigate the development of optical biometric components in children with hyperopia, and apply a machine-learning model to predict axial length.

Authors

Jingyun Wang

School of Life Science and Biotechnology , Dalian University of Technology , 2 Linggong Road , Dalian 116024 , P. R. China.
Reed M Jost

Retina Foundation of the Southwest, Dallas, TX, USA.
Eileen E Birch

Retina Foundation of the Southwest, Dallas, TX, USA.

Keywords

Anterior Chamber Axial Length, Eye Biometry Child Child, Preschool Cornea Female Humans Hyperopia Machine Learning Male Refraction, Ocular

External Resources

View on PubMed Access via DOI PubMed (38809529)

Ocular Biometric Components in Hyperopic Children and a Machine Learning-Based Model to Predict Axial Length.

Abstract

Authors

Keywords

External Resources

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