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An Open-Source Horizontal Strabismus Simulator as an Evaluation Platform for Monocular Gaze Estimation Using Deep

Shumpei Takinami^1,2, Yuka Morita¹, Jun Seita^1,2,3

¹Department of Ophthalmology, Faculty of Medicine, University of Tsukuba, Tsukuba 305-8575, Japan.

Journal of Eye Movement Research

|February 20, 2026

Summary

This summary is machine-generated.

A new, affordable simulator for horizontal strabismus was developed to test AI models. Current AI models struggle to accurately detect strabismus, highlighting the need for specialized algorithms.

Keywords:

deep learning eye tracking gaze estimation monocular vision open-source hardware simulator strabismus

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Area of Science:

Ophthalmology
Computer Vision
Biomedical Engineering

Background:

Strabismus affects 2-4% of the global population, predominantly horizontal types.
Automated screening using gaze estimation is promising but lacks validation for strabismus due to unsuitability of existing models and evaluation platforms.
Disconjugate eye movements in strabismus require specialized simulation for accurate AI model evaluation.

Purpose of the Study:

To develop an open-source, low-cost horizontal strabismus simulator.
To evaluate the performance of current AI gaze estimation models on simulated strabismus.
To establish baseline performance metrics for advancing strabismus screening technology.

Main Methods:

Developed a simulator with two independently controlled artificial eyeballs on a gimbal mechanism.
Integrated servo motors and gyro sensors for real-time angle measurement with high mechanical accuracy (<0.1° error).
Evaluated three AI models (Single Eye, GazeNet, EyeNet) using the simulator, measuring gaze estimation errors.

Main Results:

The simulator achieved high mechanical accuracy, below the clinical detection threshold.
Evaluated AI models exhibited significant gaze estimation errors (6.44-8.75°), exceeding the clinical target (2.8°).
Accuracy degraded rapidly beyond ±15° gaze angles, indicating limitations for detecting small-angle strabismus.

Conclusions:

Existing AI gaze estimation models are inadequate for strabismus screening without strabismus-specific development.
The developed simulator provides a crucial platform for evaluating and improving AI models for strabismus detection.
Further research is needed to develop specialized AI algorithms capable of accurate gaze estimation in strabismus patients.