Automated strabismus detection and classification using deep learning analysis of facial images

Mahsa Yarkheir ¹, Motahhareh Sadeghi ², Hamed Azarnoush ³

⁰Biomedical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez, P.O. Box:15875-4413, Tehran, Iran.

Scientific Reports +

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January 31, 2025

View abstract on PubMed

Summary

This study introduces a deep learning model for automatic strabismus detection from facial images. The AI achieved high accuracy in identifying eye misalignment, aiding early diagnosis and treatment planning.

Area Of Science

Ophthalmology
Artificial Intelligence
Medical Imaging

Background

Strabismus, or eye misalignment, is a prevalent condition requiring early detection for effective management.
Accurate classification of strabismus is crucial to prevent long-term visual complications.

Purpose Of The Study

To develop and evaluate a deep learning model for automated strabismus identification and classification using facial images.
To assess the model's performance in both binary (strabismus vs. normal) and multi-class (deviation angle) classification tasks.

Main Methods

Utilized Convolutional Neural Networks (CNNs) for image analysis.
Trained and validated the model on datasets comprising 4,257 images for binary classification and 622 images for multi-class classification.
Employed five-fold cross-validation and evaluated performance using accuracy, sensitivity, F1-score, and recall.

Main Results

The deep learning model achieved 86.38% accuracy for binary strabismus classification.
The model demonstrated 92.7% accuracy for multi-class classification of strabismus deviation angles.
Performance metrics confirmed the model's effectiveness in identifying and classifying strabismus.

Conclusions

The proposed deep learning approach shows significant potential for assisting healthcare professionals in the early detection of strabismus.
This technology can aid in strabismus treatment planning, potentially improving patient outcomes.
Automated analysis of facial images offers a promising tool for ophthalmological diagnostics.

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Vision

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