Artificial Intelligence (AI), Myopia, Randomized Controlled Trials
Conditions
Keywords
Myopia Prediction, Randomized Controlled Trials, Artificial Intelligence (AI)
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.
Interventions
OTHERFeedback 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.
OTHERFeedback on Ophthalmic Examinations
At baseline and six months, participants will be provided with the results of their ophthalmic examinations.
Sponsors
Zhongshan Ophthalmic Center, Sun Yat-sen University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
SINGLE (Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
9 Years to 11 Years
Healthy volunteers
Yes
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Proportion of Individuals Predicted to Develop High Myopia at Age 18 by the Myopia Prediction System | 1 year | 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. |
| Cumulative Clinical Visit Rate for Myopia Prevention and Control | Within 3 months after each intervention | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Myopia Incidence Rate | 1 year | 1-year myopia incidence rate = number of new myopia cases within one year / number of non-myopic cases at baseline \* 100% |
| Changes in Spherical Equivalent | 1 year | Change in spherical equivalent (non-cycloplegic autorefraction) will be calculated |
| Screen Time | 1 year | Daily usage time of electronic devices (computer/smartphone/tablet computer) will be calculated |
| Outdoor Activity Time | 1 year | Daily outdoor activity time will be calculated |
Countries
China
Contacts
Primary ContactYahan Yang, M.D., Ph.D
Backup ContactXinwei Chen, M.D.
Outcome results
None listed