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A computer vision-based framework for objective evaluation of sunken upper eyelid.

Longfei Weng¹, Yuchen Shen¹, Shiqi Xie^2,3

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Scientific Reports

|October 22, 2025

Summary

This study introduces a computer vision (CV) framework for objective sunken upper eyelid assessment. The method analyzes eyelid morphology from single images, enabling better evaluation of surgical outcomes.

Keywords:

Computer vision Image assessment Sunken upper eyelid Support vector machine

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Area of Science:

Ophthalmology
Computer Vision
Medical Imaging

Background:

Sunken upper eyelid correction is crucial for periorbital reconstruction.
Current evaluation methods rely on expensive equipment or subjective assessments.
Objective and accessible evaluation tools are needed.

Purpose of the Study:

To develop a computer vision (CV) framework for objective assessment of sunken upper eyelid morphology.
To provide a reliable method for evaluating surgical outcomes in periorbital reconstruction.

Main Methods:

A two-stage CV framework was developed.
Facial landmarks were detected to isolate the periocular region, followed by normalization and segmentation.
Key features including Variance of Gray Value (VGV), Structural Similarity Index (SSIM), and Degree of Eyelid Wrinkles (DEW) were extracted.
A Support Vector Machine (SVM) model integrated these features to score overall morphology.

Main Results:

Significant differences in VGV, SSIM, and DEW were observed between normal and patient groups.
The proposed method demonstrated measurable improvements in postoperative surgical outcomes.
The SVM model output, L2 distance to the separating hyperplane (D(f)), effectively scored morphological features.

Conclusions:

The CV framework offers an objective and accessible method for assessing sunken upper eyelid morphology.
This approach can aid in evaluating the effectiveness of surgical interventions in periorbital reconstruction.
The method shows potential for improving clinical decision-making and patient care.