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Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks.

Zenghai Chen¹, Hong Fu¹, Wai-Lun Lo¹

¹Department of Computer Science, Chu Hai College of Higher Education, 80 Castle Peak Road, Castle Peak Bay, Tuen Mun, NT, Hong Kong.

Journal of Healthcare Engineering

|June 2, 2018

Summary

This summary is machine-generated.

This study introduces an automatic method for strabismus recognition using eye-tracking data and convolutional neural networks (CNNs). The approach effectively identifies strabismus, improving diagnostic efficiency and reducing costs.

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Area of Science:

Ophthalmology
Computer Vision
Medical Diagnostics

Background:

Strabismus is a common vision disorder leading to amblyopia and potential vision loss.
Early diagnosis of strabismus is critical for effective treatment outcomes.
Manual strabismus diagnosis is labor-intensive and inefficient.

Purpose of the Study:

To develop an automated method for strabismus recognition using eye-tracking data.
To enhance the efficiency and reduce the cost of strabismus diagnosis.

Main Methods:

Eye-tracking data was collected from subjects using an eye tracker.
A gaze deviation (GaDe) image was created to represent eye-tracking data characteristics.
A convolutional neural network (CNN) pre-trained on ImageNet was utilized for feature extraction and classification.

Main Results:

Natural image features were successfully transferred to represent eye-tracking data.
The proposed method effectively recognized strabismus in experimental datasets.
The CNN model demonstrated strong performance in classifying strabismic versus normal subjects.

Conclusions:

Automated strabismus recognition using eye-tracking data and CNNs is feasible and effective.
The GaDe image representation combined with CNNs offers a promising approach for strabismus diagnosis.
This method has the potential to significantly improve diagnostic efficiency in clinical settings.