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Related Experiment Video

Updated: Jan 16, 2026

Using an Automated Hirschberg Test App to Evaluate Ocular Alignment
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Development of an embedded diagnostic tool for visual misalignment screening.

Daniel Soto Rodriguez1, Andres Eduardo Rivera Gomez2, Ruthber Rodriguez Serrezuela3

  • 1Facultad de ingeniería, ingeniería de software, universidad surcolombiana, Neiva, Huila, Colombia.

Hardwarex
|October 1, 2025
PubMed
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This study introduces a low-cost, AI-powered system for early strabismus detection using computer vision. The portable device achieves high accuracy in screening for eye conditions, making it ideal for underserved areas.

Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Ophthalmology

Background:

  • Strabismus, or eye misalignment, requires early detection for effective treatment.
  • Existing screening methods can be costly and inaccessible, especially in low-resource settings.
  • Computer vision and deep learning offer potential for automated, low-cost diagnostic tools.

Purpose of the Study:

  • To design, implement, and validate an affordable embedded system for preliminary strabismus screening.
  • To leverage computer vision and deep learning for accurate and real-time eye condition analysis.
  • To create a portable, user-friendly prototype for accessible health screening and education.

Main Methods:

  • Developed a hardware system using Raspberry Pi 4, USB camera, and 3D-printed chin rest.
Keywords:
Convolutional neural networks (CNN)Low-cost diagnostic deviceMedical AIStrabismus detection

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  • Implemented software in Python with OpenCV and a NASNetLarge deep learning model (TensorFlow Lite).
  • Validated the system using proprietary and balanced datasets, including 10-fold cross-validation.

    • Main Results:

    • Achieved 96.30% classification accuracy on a proprietary dataset (27 images).
    • Demonstrated 95.6% average accuracy with strong generalization (F1-score, precision, recall >94%) on a 1000-image dataset.
    • Validated a novel treatment mechanism for reliable eye tracking and microstrabismus detection.

    Conclusions:

    • The developed open-source system offers a low-cost, accurate solution for preliminary strabismus screening.
    • Its portability and ease of use make it suitable for community health initiatives and educational purposes.
    • The system shows significant potential for improving accessibility to eye care, particularly in resource-limited environments.