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Innovative AI-based system for precision diagnosis of childhood strabismus incorporating gaze tracking and real-time
1Department of Ophthalmology, College of Medicine, Qassim University, Qassim, Saudi Arabia.
Insights
A new AI system accurately diagnoses pediatric strabismus using eye-tracking and image data. This AI tool also provides therapeutic benefits, improving eye alignment and fusion while reducing evaluation time.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Strabismus is a common pediatric eye condition with potential long-term developmental and psychosocial effects.
- Current diagnostic methods rely on clinician expertise, leading to variability and potential delays in treatment.
- Objective diagnostic tools are needed to improve accuracy and efficiency in pediatric strabismus care.
Purpose of the Study:
- To develop and evaluate a multimodal artificial intelligence (AI) system for diagnosing pediatric strabismus.
- To assess the AI system's diagnostic accuracy compared to pediatric ophthalmologists and existing AI methods.
- To investigate the therapeutic impact and operational efficiency of an integrated AI-driven diagnostic and training system.
Main Methods:
- A prospective diagnostic accuracy trial involving 250 children (aged 3-10 years).
- Collection of eye-tracking and image data using a Tobii Pro Fusion device.
- Analysis via a dual-stream deep learning model (ResNet-50 and 1D-CNN) with multimodal fusion.
Main Results:
- The AI system achieved high diagnostic accuracy: 96.8% binary and 92.5% multi-class, with strong agreement (κ=0.931) with gold-standard diagnoses.
- Therapeutic evaluation demonstrated a 30.1% reduction in deviation angle and a 17% improvement in fusion, with high adherence (87%).
- Diagnostic efficiency significantly improved, reducing evaluation times by 62.1% and increasing parental satisfaction.
Conclusions:
- The multimodal AI system offers diagnostic accuracy comparable to specialist evaluation for pediatric strabismus.
- The integrated system provides measurable therapeutic benefits and enhances operational efficiency in eye care.
- This AI approach presents a patient-centered solution for pediatric strabismus, suitable for diverse clinical settings.
Abstract:
PurposeStrabismus is a common pediatric eye disorder that can lead to developmental and psychosocial consequences if not treated promptly. Traditional diagnostic methods often depend on clinician expertise, which can result in variability and delayed intervention.MethodsA prospective diagnostic accuracy trial was conducted with 250 children aged 3 to 10 years. Eye-tracking and image data were collected using a Tobii Pro Fusion device and analyzed using a dual-stream deep learning model (ResNet-50 combined with a 1D-CNN). The diagnostic performance was compared against pediatric ophthalmologists and existing AI methods. A real-time feedback module was implemented to provide vergence and anti-suppression training, enabling assessment of its therapeutic impact.ResultsThe AI system achieved a binary accuracy of 96.8% (AUC = 0.992) and a multi-class accuracy of 92.5% (AUC = 0.983), demonstrating agreement with gold-standard diagnoses (κ = 0.931). Ablation studies indicated that the multimodal fusion performed better than unimodal baselines (p < 0.01). Therapeutic evaluation showed a 30.1% reduction in deviation angle, a 17% improvement in fusion, and high adherence rates (87%). Diagnostic efficiency improved, with evaluation times reduced by 62.1% (p < 0.001), and parental satisfaction scores increased (p < 0.01).ConclusionThe findings indicate that the proposed multimodal AI system provides diagnostic accuracy comparable to specialist evaluation, along with measurable therapeutic benefits and improved operational efficiency. By integrating diagnostic and rehabilitative functions, the system presents a patient-centered approach to pediatric strabismus care with potential for application in clinical and resource-limited settings.
