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Pilot Evaluation of a Web Application for Amblyopia Risk Screening Integrating Parent-Reported Factors with
Mustapha Jaouhari1, Chaimae El Harrak1, Farida Bentayeb2
1Laboratory of Electronic, Mechanical, and Energetic Information Processing Systems, Faculty of Sciences, Ibn Tofail University, Morocco.
Insights
A new web-based tool accurately identifies children at high risk for amblyopia using AI and parent-reported factors. This screening method is effective for resource-limited settings, ensuring early detection of visual impairment.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Pediatric Health
Background:
- Amblyopia is the leading cause of childhood visual impairment.
- Limited access to eye care specialists hinders early detection in many regions.
- Effective screening tools are crucial for timely intervention.
Purpose of the Study:
- To assess the accuracy of a novel web-based screening tool for identifying children at risk of amblyopia.
- The tool integrates parent-reported risk factors and AI-driven strabismus detection.
- To evaluate the tool's potential in resource-limited settings.
Main Methods:
- A pilot study involved 105 children (aged 3-10) in Morocco.
- Parents completed an online questionnaire on amblyopia risk factors.
- AI analyzed smartphone photos for strabismus, using CNNs and geometric measurements.
- Children were categorized into high, moderate, or low-risk groups based on a total score.
Main Results:
- The tool achieved 100% positive predictive value (PPV) for high-risk children, all confirmed with amblyopia.
- No low-risk children were diagnosed with amblyopia (100% negative predictive value).
- The AI strabismus module showed 96.9% accuracy in the high-risk group.
Conclusions:
- The hybrid screening tool reliably identifies high-risk children for amblyopia, aligning with clinical diagnoses.
- It effectively excludes low-risk children, demonstrating safety.
- This accessible, low-cost approach is promising for pediatric vision screening in underserved areas.
- Further large-scale studies are needed to confirm generalizability.
Background:
Amblyopia is the most common cause of visual impairment in children, and early detection is essential, yet screening remains limited in many settings, especially where access to eye-care specialists is scarce.
Objective:
To evaluate the accuracy of a web-based screening tool that combines parent-reported risk factors with AI-assisted strabismus detection for identifying children at risk of amblyopia.
Methods:
This pilot study included 105 children aged 3-10 years attending a public hospital in Morocco for their first ophthalmological evaluation. Parents completed an online screening tool consisting of eight validated amblyopia risk-factor questions and an automated strabismus analysis based on a frontal smartphone photograph. The AI module combined geometric measurements of pupil-nasal root symmetry with convolutional neural network (CNN) features such as corneal light reflex and gaze vector orientation. Each child received a total score (0-9), stratified into high-risk (6-9), moderate-risk (3-6), or low-risk (0-3) categories. A comprehensive ophthalmological examination, performed by a clinician blinded to the application results, served as the reference standard.
Results:
Of the 105 children screened, 32 were classified as high-risk, 62 as moderate-risk, and 11 as low-risk. The tool demonstrated perfect agreement in the high-risk category, with all 32 high-risk children clinically confirmed to have amblyopia (PPV = 100%). In the moderate-risk group, 30 of the 62 children were clinically confirmed (PPV = 48.4%). No child in the low-risk group had amblyopia (NPV = 100%). The AI-assisted strabismus module showed strong predictive accuracy in the high-risk category (96.9% confirmation). Statistical analyses showed no significant differences in diagnostic performance across age, gender, or urban/rural subgroups (p > 0.05).
Conclusions:
The hybrid screening tool reliably identified children at high risk for amblyopia with complete concordance with a blinded clinical diagnosis, while safely excluding low-risk children. Although moderate-risk scores require cautious interpretation and clinical follow-up, this approach offers a low-cost, accessible, and scalable solution for paediatric vision screenings in resource-limited settings. Further large-scale community-based studies are warranted to validate generalisability.
