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Efficient strabismus diagnosis from small samples: Harnessing spatial features for improved accuracy.

Renzhong Wu¹, Shenghui Liao¹, Yongrong Ji²

¹School of Computer Science and Engineering, Central South University, Changsha, 410083, China.

Journal of Biomedical Informatics

|December 12, 2024

Summary

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This summary is machine-generated.

A new computer-aided model, RIS-MLP, efficiently diagnoses strabismus using facial images. This method improves accessibility for early detection, preventing vision impairment and loss of stereopsis.

Area of Science:

Ophthalmology
Computer Vision
Medical Imaging

Background:

Strabismus diagnosis is critical for preventing visual impairment and loss of stereopsis.
Traditional diagnosis methods require specialized equipment and personnel, limiting accessibility.
Computer-aided diagnosis offers an efficient alternative for strabismus detection.

Purpose of the Study:

To develop an efficient strabismus diagnosis model, RIS-MLP, using minimal data from frontal facial images.
To enhance strabismus diagnosis accuracy and accessibility through a novel computer-aided approach.

Main Methods:

Designed the RIS-MLP model incorporating light reflex and iris detection modules.
Utilized frontal facial images captured under natural lighting conditions via the Hirschberg test.

Keywords:

Hirschberg test Medical image processing Small samples learning Spatial features Strabismus diagnosis

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Employed optimized spatial feature strategies for improved classification performance.

Main Results:

The RIS-MLP model demonstrated superior sample efficiency in indirect comparative experiments.
Direct comparisons showed the RIS-MLP mitigates overfitting and outperforms state-of-the-art models on noisy, imbalanced datasets.
Variants like RIS-SVM also showed strong performance.

Conclusions:

The RIS-MLP model provides an efficient and accurate method for strabismus diagnosis.
This computer-aided approach enhances diagnostic accessibility, especially with limited or challenging data.
The model shows promise for real-world applications in ophthalmology.