Skip to content

Study on the Use of Artificial Intelligence to Track and Predict Eye Power Changes in School-Going Children

Development of an Artificially Intelligent Tool for Analysis and Prediction of Myopia Progression Among School-Going Children - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2025/07/091243
Enrollment
12000
Registered
2025-07-21
Start date
Unknown
Completion date
Unknown
Last updated
2025-08-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: H521- Myopia

Interventions

Intervention1: Nil : AI model trained using ocular, behavioral, and environmental data (e.g., axial length, screen time, parental myopia) to predict myopia progression. No therapeutic intervention adm

Sponsors

Indian Council of Medical Research (ICMR)
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: School-going children aged 6 to 18 years Enrolled in selected schools across urban and rural areas of Bhopal Division (Madhya Pradesh) Parental or legal guardian consent and age-appropriate child assent provided Willing to participate in ocular assessments and periodic follow-ups over 24 months

Exclusion criteria

Exclusion criteria: History of ocular trauma, congenital anomalies, or prior eye surgeries Children with systemic illnesses affecting vision (e.g., diabetes, neurological disorders) Inability to cooperate with eye exams or data collection Already enrolled in another interventional ophthalmic study

Design outcomes

Primary

MeasureTime frame
Change in Spherical Equivalent Refraction (SER) to quantify progression of myopia. Change in Axial Length to assess ocular growth associated with myopia progression. Time Points for Assessment: Baseline (at enrolment) 6 months from baseline 12 months from baselinTimepoint: Outcome: Change in Spherical Equivalent Refraction (SER) and Axial Length used to measure and quantify myopia progression in school-going children. Time Points: At baseline (enrolment), at 6 months, and at 12 months.

Secondary

MeasureTime frame
AI Model Performance Metrics Accuracy, Sensitivity, Specificity, AUROC, F1 Score Timepoint: After each longitudinal data collection point 6, 12, 18, and 24 months;Identification and Ranking of Dominant Risk Factors (e.g., screen time, parental myopia, near work duration) Timepoint: At end of Phase 1 (Baseline cross-sectional analysis);Validation Accuracy of Mobile Application Predictions vs. clinical diagnoses (Positive Predictive Value, Kappa score) Timepoint: 18 and 24 months (during pilot testing of the mobile app);Awareness and Behavior Change Metrics Number of children reached, engagement in prevention programs Timepoint: Ongoing throughout study; evaluated cumulatively at 12, 18, and 24 months

Countries

India

Contacts

Public ContactDr Priti Singh

All India Institute of Medical Sciences, Bhopal

priti.ophtho@aiimsbhopal.edu.in9993896479

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Apr 16, 2026