DL Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults
Efficacy of Deep Learning Models for Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Adults With Myopia
Second Affiliated Hospital of Nanchang University
2,500 participants
Oct 3, 2023
OBSERVATIONAL
Conditions
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.
Eligibility
Plain Language Summary
Simplified for easier understanding
This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.
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Interventions
The machine learning model was applied to each participant's non-cycloplegic parameters to predict cycloplegic spherical equivalent.
Locations(1)
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NCT07690813