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RECRUITING
NCT07690813

DL Models Predicting Cycloplegic Refractive Error Based on Non-Cycloplegic Parameters in Myopic Adults

Sponsor: Second Affiliated Hospital of Nanchang University

View on ClinicalTrials.gov

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

DIAGNOSTIC_TEST

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