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

NCT07690813 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2500

Last updated 2026-07-09

No results posted yet for this study

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.

Conditions

  • Refractive Errors
  • Cycloplegic Refraction
  • Accommodation

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.

Sponsors & Collaborators

  • Second Affiliated Hospital of Nanchang University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
47 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-10-03
Primary Completion
2026-11-25
Completion
2026-11-25

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07690813 on ClinicalTrials.gov