Clinical Research Directory
Browse clinical research sites, groups, and studies.
DL Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults
Sponsor: Second Affiliated Hospital of Nanchang University
Summary
This study presents a machine learning model that predicts cycloplegic refraction in adults with myopia using standard non-cycloplegic eye measurements, aiming to reduce the need for cycloplegic drops while still identifying patients who require them.
Official title: Efficacy of Deep Learning Models for Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Adults With Myopia
Key Details
Gender
All
Age Range
18 Years - 47 Years
Study Type
OBSERVATIONAL
Enrollment
2500
Start Date
2023-10-03
Completion Date
2026-11-25
Last Updated
2026-07-09
Healthy Volunteers
No
Interventions
Machine learning model for predicting cycloplegic refraction
The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.
Locations (1)
The Second Affiliated Hospital of Nanchang University, Nanchang, JiangXi 330000
Jiangxi, China