Clinical trial NCT06633393: Effect of a Myopia Prediction System on Myopia Prevention and Control
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
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Myopia
Randomized Controlled Trials
Artificial Intelligence (AI)
Keywords: Myopia Prediction
Randomized Controlled Trials
Artificial Intelligence (AI)
Lead sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
Sponsor class: OTHER
First posted: 2024-10-09
Enrollment: 20000
Sex: ALL
Minimum age (years): 9
Maximum age (years): 11
Standard ages: CHILD
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

Brief summary: 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.


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)

Primary outcomes:
measure: Proportion of Individuals Predicted to Develop High Myopia at Age 18 by the Myopia Prediction System
timeFrame: 1 year
measure: Cumulative Clinical Visit Rate for Myopia Prevention and Control
timeFrame: Within 3 months after each intervention
Secondary outcomes:
measure: Myopia Incidence Rate
timeFrame: 1 year
measure: Changes in Spherical Equivalent
timeFrame: 1 year
measure: Screen Time
timeFrame: 1 year
measure: Outdoor Activity Time
timeFrame: 1 year
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Zhongshan Ophthalmic Center, Sun Yat-sen University
state: Guangdong
Study officials:
affiliation: Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong
name: Haotian Lin, M.D., Ph.D
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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. 2018 Nov 6;15(11):e1002674. doi: 10.1371/journal.pmed.1002674. eCollection 2018 Nov.