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Related Experiment Video

Updated: Jun 6, 2025

Using an Automated Hirschberg Test App to Evaluate Ocular Alignment
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Strabismus Detection in Monocular Eye Images for Telemedicine Applications.

Wattanapong Kurdthongmee1, Lunla Udomvej2, Arsanchai Sukkuea1

  • 1School of Engineering and Technology, Walailak University, Thai Buri, Thasala, Nakornsithammarat 80160, Thailand.

Journal of Imaging
|November 26, 2024
PubMed
Summary

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This study introduces a new machine learning method for early strabismus detection using synchronized eye movements. This telemedicine-compatible tool offers non-invasive, efficient screening for improved patient outcomes.

Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Strabismus is a common eye misalignment disorder affecting vision.
  • Early detection is crucial for effective treatment and preventing long-term visual impairment.
  • Current diagnostic methods may have limitations in accessibility and efficiency, especially in telemedicine settings.

Purpose of the Study:

  • To develop and validate a novel, non-invasive method for the early detection of strabismus.
  • To adapt this method for telemedicine applications, enhancing accessibility to eye care.
  • To utilize machine learning for accurate strabismus identification based on eye movement analysis.

Main Methods:

  • Employing synchronized eye movements to estimate pupil location.
Keywords:
early detectionocular misalignmentscreeningstrabismustelemedicine

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  • Developing individual regression models for each eye using advanced machine learning algorithms.
  • Identifying strabismus by detecting significant discrepancies between estimated and actual pupil positions.

    • Main Results:

    • The developed method accurately estimates pupil location in non-strabismic cases.
    • Significant deviations between estimated and actual pupil positions reliably indicate strabismus.
    • The machine learning models demonstrate high efficacy in differentiating strabismic from non-strabismic individuals.

    Conclusions:

    • The novel method offers a non-invasive and efficient approach for early strabismus detection.
    • The technique is suitable for telemedicine, broadening access to ophthalmic screening.
    • This machine learning-based tool facilitates timely intervention, potentially improving patient outcomes, especially in pediatric populations.