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AI-Assisted Strabismus Diagnosis Using Eye-Tracking and Machine Learning.

¹The Future Artificial Intelligence Technology Company, Jeonju 54893, Republic of Korea.

Diagnostics (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

This study introduces an AI framework using eye-tracking for objective strabismus diagnosis, improving upon the traditional Alternate Cover Test (ACT). The system achieved high accuracy, offering a standardized and reliable clinical screening tool.

Keywords:

AI-assisted diagnosis clinical decision support eye tracking machine learning ophthalmic imaging strabismus

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Area of Science:

Ophthalmology
Artificial Intelligence
Biomedical Engineering

Background:

The Alternate Cover Test (ACT) for strabismus diagnosis lacks quantitative standardization.
Objective diagnostic methods are needed to reduce clinical bias and improve reliability.

Purpose of the Study:

To develop and evaluate an AI-assisted framework for objective strabismus screening.
To standardize the interpretation of the ACT using eye-tracking and machine learning.

Main Methods:

Gaze coordinates were collected using a 60 Hz infrared eye tracker during the ACT.
Data from 50 participants (335 valid samples) were used to train and test seven machine learning algorithms.
Performance was assessed using accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Main Results:

The Random Forest algorithm achieved the highest performance on the test set.
Achieved 97.56% accuracy, 1.00 sensitivity, 0.95 specificity, 0.95 PPV, and 1.00 NPV.
The confusion matrix indicated minimal false negatives, confirming the system's reliability for clinical screening.

Conclusions:

The proposed AI framework offers a robust and objective tool for strabismus diagnosis.
It standardizes ACT interpretation and significantly reduces clinical bias.
This technology enhances the reliability of strabismus screening in clinical settings.