Effect of a Myopia Prediction System on Myopia Prevention and Control

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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Myopia; Randomized Controlled Trials; Artificial Intelligence (AI)
• Intervention / Treatment: Other: Feedback on Predicted High Myopia Risk at Age 18 Using the Myopia Prediction System; Other: Feedback on Ophthalmic Examinations

• Study Type: Interventional
• Enrollment (Estimated): 20000
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Yahan Yang, M.D., Ph.D; Phone Number: +86 15521013933; Email: yah.yang39@qq.com
• Study Contact Backup: Name: Xinwei Chen, M.D.; Phone Number: +86 13535382011; Email: cxw20000709@163.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China
      • Zhongshan Ophthalmic Center, Sun Yat-sen University
      • Contact: Haotian Lin, M.D., Ph.D; Phone Number: +86 13802793086; Email: linht5@mail.sysu.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: Yes

Description

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)

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Experimental Arm; At baseline and six months, feedback on ophthalmic examination results and future high myopia risk at 18 years of age using the myopia prediction system	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.
Active Comparator: Control Arm; At baseline and six months, feedback on ophthalmic examination results.	Other: Feedback on Ophthalmic Examinations; At baseline and six months, participants will be provided with the results of their ophthalmic examinations.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Proportion of Individuals Predicted to Develop High Myopia at Age 18 by the Myopia Prediction System	At the end of the one-year study, the Myopia Prediction System will be used to predict whether students will develop high myopia at age 18 in both the intervention and control groups. The Proportion of Individuals Predicted to Develop High Myopia at Age 18 by the Myopia Prediction System is calculated as the total number of students in each group predicted to develop high myopia by age 18, divided by the total number of students in the respective group.	1 year
Cumulative Clinical Visit Rate for Myopia Prevention and Control	The Cumulative Clinical Visit Rate Proportion of Clinical Visits for Myopia Prevention and Control is the proportion of students in the intervention or control group who visited a hospital or clinic for myopia-related care (e.g., refractive exams and treatment) at least once within three months of either intervention. It is calculated as the number of students in each group who attended a clinical visit within three months of at least one intervention, divided by the total number of students in the respective group.	Within 3 months after each intervention

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Myopia Incidence Rate	1-year myopia incidence rate = number of new myopia cases within one year / number of non-myopic cases at baseline * 100%	1 year
Changes in Spherical Equivalent	Change in spherical equivalent (non-cycloplegic autorefraction) will be calculated	1 year
Screen Time	Daily usage time of electronic devices (computer/smartphone/tablet computer) will be calculated	1 year
Outdoor Activity Time	Daily outdoor activity time will be calculated	1 year

Collaborators and Investigators

• Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
• Investigators: Principal Investigator: Haotian Lin, M.D., Ph.D, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong

Publications and helpful links

General 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. 2018 Nov 6;15(11):e1002674. doi: 10.1371/journal.pmed.1002674. eCollection 2018 Nov.

Study record dates

Study Major Dates

• Study Start (Estimated): November 1, 2024
• Primary Completion (Estimated): December 31, 2025
• Study Completion (Estimated): February 28, 2026

Study Registration Dates

• First Submitted: October 8, 2024
• First Submitted That Met QC Criteria: October 8, 2024
• First Posted (Actual): October 9, 2024

Study Record Updates

• Last Update Posted (Actual): October 15, 2024
• Last Update Submitted That Met QC Criteria: October 9, 2024
• Last Verified: September 1, 2024

More Information

Terms related to this study

• Keywords: Randomized Controlled Trials; Artificial Intelligence (AI); Myopia Prediction
• Additional Relevant MeSH Terms: Eye Diseases; Refractive Errors; Myopia
• Other Study ID Numbers: 2024KYPJ102

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No