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Related Experiment Video

Updated: Jun 26, 2026

Using an Automated Hirschberg Test App to Evaluate Ocular Alignment

Using an Automated Hirschberg Test App to Evaluate Ocular Alignment

Published on: March 24, 2020

StrabNet-CQ: an integrated deep learning framework for automated strabismus classification and quantification using

Shubh Garg¹, Ashish Sunkarapalli², Debabrata Ghosh¹

¹Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India.

BMC Ophthalmology

|October 23, 2025

Summary

This summary is machine-generated.

StrabNet-CQ, a deep learning framework, accurately detects and classifies strabismus using eye images. This automated system offers objective quantification, improving upon traditional methods.

Keywords:

Artificial intelligence Classification Deep learning Landmark detection Ocular deviation Ocular misalignment Strabismus Strabismus quantification

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Area of Science:

Ophthalmology and Computer Vision
Artificial Intelligence in Healthcare

Background:

Strabismus, an ocular misalignment, affects binocular vision and is traditionally diagnosed using manual prism diopter (PD) readings.
Current diagnostic methods are subjective, prone to inter-clinician variability, and provide coarse measurements of deviation.

Purpose of the Study:

To develop and evaluate StrabNet-CQ, a deep learning framework for automated strabismus classification and quantification.
To provide an objective and precise alternative to conventional strabismus diagnostic techniques.

Main Methods:

Utilized a deep learning framework (StrabNet-CQ) analyzing 600 eye images.
Employed YOLOv8 for initial classification (normal/abnormal, specific types) and ResNet101 for refined classification on segmented eye regions.
Used ResNet18 for ocular landmark detection to compute deviation indices and angular deviation.

Main Results:

Achieved 94% accuracy in strabismus detection and 90% in classification.
Demonstrated high sensitivity for normal, esotropia, and hypotropia.
Derived parameters correlated well (r=0.733) with manual PD values, enabling quantification.

Conclusions:

StrabNet-CQ offers objective strabismus diagnosis and quantification.
The framework shows potential for clinical deployment, enhancing strabismus detection and measurement.