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Not yet recruiting NCT06633393

Effect of a Myopia Prediction System on Myopia Prevention and Control

No phase Interventional Myopia Randomized Controlled Trials Artificial Intelligence (AI)

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Feedback on Predicted High Myopia Risk at Age 18 Using the Myopia Prediction System, Feedback on Ophthalmic Examinations.
Who it may be relevant to
Registry conditions: Myopia, Randomized Controlled Trials, Artificial Intelligence (AI). Basic parameters: 9 years — 11 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

Impact of Feedback Based on the Myopia Prediction System on High Myopia Risk and Consultation Behavior in School-aged Children: a Cluster Randomized Controlled Trial

Overview

The global rise in myopia, particularly among children and adolescents in China, underscores the inadequacy of current prevention strategies, indicating that conventional screening and education alone are insufficient to curb the prevalence. Integrating personalized myopia prediction into routine care may enhance risk awareness, promote proactive prevention, and improve adherence to medical advice, ultimately reducing the future burden of high myopia. A myopia prediction system based on artificial intelligence was previously developed, accurately predicting future high myopia risk using efficient, robust, and easily accessible predictive factors, including age, spherical equivalent, and the annual progression of spherical equivalent. This study aims to conduct a prospective, one-year, cluster randomized controlled clinical trial to investigate the effectiveness of this prediction system in preventing and controlling myopia in school-aged children.

Interventions

  • Other Feedback on Predicted High Myopia Risk at Age 18 Using the Myopia Prediction System
    At baseline and six months, participants will be provided with the results of their predicted risk of high myopia at age 18 based on the myopia prediction system.
  • Other Feedback on Ophthalmic Examinations
    At baseline and six months, participants will be provided with the results of their ophthalmic examinations.

Primary outcome measures

  • Proportion of Individuals Predicted to Develop High Myopia at Age 18 by the Myopia Prediction System [Time frame: 1 year]
  • Cumulative Clinical Visit Rate for Myopia Prevention and Control [Time frame: Within 3 months after each intervention]
Secondary outcome measures (4)
  • Myopia Incidence Rate [Time frame: 1 year]
  • Changes in Spherical Equivalent [Time frame: 1 year]
  • Screen Time [Time frame: 1 year]
  • Outdoor Activity Time [Time frame: 1 year]

Eligibility criteria

Inclusion criteria

  • The participant and their guardian voluntarily signed the informed consent form
  • Has the record of eye refraction examination in the past year
  • Aged 9 to 11 years, regardless of gender

Exclusion criteria

  • High myopia(spherical equivalent ≤ -6.00 D)
  • Ocular diseases other than myopia (e.g., strabismus, amblyopia, congenital cataract, juvenile glaucoma, retinal diseases).
  • Systemic diseases that may affect vision or visual development (e.g., diabetes or other endocrine disorders, cardiovascular or respiratory diseases, Down syndrome)

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Single blind
Primary purpose
Other

Study locations

China · 1 center
  • Zhongshan Ophthalmic Center, Sun Yat-sen University — Guangzhou

Publications

  • Lin H, Long E, Ding X, Diao H, Chen Z, Liu R, Huang J, Cai J, Xu S, Zhang X, Wang D, Chen K, Yu T, Wu D, Zhao X, Liu Z, Wu X, Jiang Y, Yang X, Cui D, Liu W, Zheng Y, Luo L, Wang H, Chan CC, Morgan IG, He M, Liu Y. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study. PLoS Med. PMID 30399150

Identifiers

NCT: NCT06633393 · 2024KYPJ102

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗