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

Longfei Weng, Yuchen Shen, Shiqi Xie, Meng Xun, Zhaohui Bu, Hui Chen, Guichuan Chen, Li Ding

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

Objective quantification of chemotherapy-induced madarosis: a pilot study of an automated computer vision pipeline for eyebrow density assessment.Scientific reports · 2026
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5 · Who and what money

Authors and funding

8 authors.

Longfei Weng *School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Yuchen Shen *School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Shiqi Xie *Department of Ophthalmology,Shanghai General Hospital, Shanghai, 200093, China.

Meng XunSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Zhaohui BuSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

Hui ChenSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. 352477354@qq.com.

Guichuan ChenChongqing Institute of Meteorological Sciences, Chongqing, 401147, China. cgccq@163.com.

Li DingSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. sunnylding@usst.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sunken upper eyelid correction is clinically and aesthetically important in periorbital reconstruction. However, current evaluation methods for sunken upper eyelid are limited by the expensive and complex medical equipment or subjective judgment. This paper proposes a single image assessment framework based on computer vision (CV) that enables objective evaluation of sunken upper eyelid morphology. The method consists of two stages: The first stage is performed to establish a consistent analysis foundation. Facial landmarks are detected using a 106-point model to crop the periocular region, followed by resolution normalization and eyelid subregion segmentation. In the second stage, three clinically relevant features are extracted: Variance of Gray Value (VGV) for surface depression, Structural Similarity Index (SSIM) for bilateral symmetry, and Degree of Eyelid Wrinkles (DEW) for texture irregularity. These features are integrated into a Support Vector Machine (SVM) model, which outputs the L2 distance to the separating hyperplane (

Indexed as

BlepharoplastyEyelidsImage Processing, Computer-AssistedAnatomic LandmarksEstheticsFaceHumansPhotographySkin AgingSupport Vector MachineTreatment OutcomeComputer visionImage assessmentSunken upper eyelidSupport vector machine

Identifiers

PMID41125792

DOI10.1038/s41598-025-20923-9

PMCPMC12546936

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.