AI-Stratified Myopia Control for Children in Remote Areas

Condition(s) studied

Investigational drug(s) / intervention(s)

Routine Screening and Referral: Based on routine screening combining uncorrected visual acuity with non-cycloplegic autorefraction and axial length, the control group provides parents with screening reports, feedback, and referrals according to the national health industry standard, which provides tiered recommendations ranging from routine monitoring and eye hygiene, to ophthalmologic consultation for those at risk of myopia, to regular 3- to 6-month re-examination for those with reduced uncorrected but normal corrected visual acuity, and to timely referral for those identified as screening-positive or with possible visual function abnormalities.

AI-Guided Tiered Management: Students in the intervention group receive routine vision screening followed by AI-based risk stratification, which classifies each student into one of three categories, hyperopia, pre-myopia, or myopia, to guide subsequent tiered interventions. For children with hyperopia, the intervention consists of prescribed outdoor activities, with a target of 2 hours per day on weekends and daily activity records. For those with pre-myopia, the intervention combines plano defocus lenses (A.M.L.™) worn for at least 8 hours per day with the same outdoor activity regimen. For those with myopia, the intervention includes defocus lenses (A.M.L.™) worn for at least 12 hours per day, also combined with outdoor activities and records. All tiered assignments are dynamically adjusted at each follow-up visit based on the most recent AI classification.

Study summary

This cluster-randomized controlled trial aims to evaluate the effectiveness and cost-effectiveness of an artificial intelligence-based, risk-stratified, tiered intervention model for childhood myopia prevention and control in resource-limited county areas of China, compared with the conventional screening-and-referral approach, with schools in project counties of Guizhou or Yunnan Province randomly assigned at a 1:1 ratio to either the intervention or control group over a 12-month follow-up period.

The study is motivated by the pressing reality that myopia among Chinese children is not only widespread but also occurring at younger ages than ever before, bringing with it a lifelong risk of sight-threatening complications, yet in remote county areas, routine vision screening programs are not yet widely available or well-established, due to a shortage of trained eye care professionals, the practical difficulties of performing cycloplegic refraction in school settings, and the lack of systematic follow-up, all of which make it hard to identify children with myopia or pre-myopia. Given that artificial intelligence may offer a practical way to assess risk without the need for pupil dilation, this study will enroll children in grades 1 through 3 from local primary schools. Over 12 months, an AI model will dynamically categorize children based on screening results and guide management adjustments(outdoor activity and defocus lenses) for the intervention group, while the control group receives routine care.

The findings are expected to provide evidence to inform and optimize AI-based myopia prevention and control strategies in remote areas.

Eligibility

Primary outcome measure(s)

• Rapid refractive progression — Baseline to 12 months
  The rapid refractive progression is defined as axial length elongation \>0.2 mm over 12 months.

Trial sites (3)

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