Evidence map›Paper›PMID 41634468›Full record

ArticleJournal of medical systems2026

Deep Learning-based Assessment of Eyelid and Periorbital Parameters: Assisting Diagnosis and Treatment Planning in Blepharoptosis.

Pengjie Chen, Lixia Lou, Shengqiang Shi, Ji Shao, Yiming Sun, Huimin Li, Xuan Zhang, Yilu Cai, Ziying Zhou, Xingru Huang and 1 more

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Article in Journal of medical systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Pengjie Chen *Zhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China.
Lixia Lou *Zhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China.
Shengqiang ShiSchool of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, China.
Ji ShaoZhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China.
Yiming SunZhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China.
Huimin LiZhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China.
Xuan ZhangZhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China.
Yilu CaiZhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China.
Ziying ZhouZhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China.
Xingru HuangSchool of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, China. xingru.huang@qmul.ac.uk.
Juan YeZhejiang University, Eye Center of Second Affiliated Hospital, School of Medicine, Hangzhou, China. yejuan@zju.edu.cn.

Funding

hejiang Provincial Natural Science Foundation of China ZCLMS25H1202Key Program of the National Natural Science Foundation of China 82330032Key Research and Development Program of Zhejiang Province 2024C03204National Natural Science Foundation Regional Innovation and Development Joint Fund U20A20386Scientific Research Fund of Zhejiang University XY2024033
6 · The paper itself

Abstract

Blepharoptosis is a common eyelid disorder that impairs both vision and appearance, requiring accurate assessment for effective treatment. This study aimed to develop and evaluate a deep learning (DL)-based system for automatic measurement of eyelid and periorbital parameters and for classifying levator function (LF) in patients with blepharoptosis. We retrospectively included 1,177 patients who underwent ptosis surgery at a tertiary oculoplastic center from January 2016 to November 2021. LF was categorized into good (> 10 mm), fair (4-10 mm), and poor (≤ 4 mm) based on clinical evaluation. The DL model segmented eyelid and eyebrow regions and measured key parameters; manual measurements were performed for comparison. A multinomial logistic regression model incorporating DL-derived features and demographic data was used to predict LF grades. The DL system achieved high segmentation performance (Dice coefficient = 0.910) and strong agreement with manual measurements (ICC = 0.988 for MRD1; 0.902 for CBH). The regression model classified LF grades with an overall accuracy of 0.760 and an AUC of 0.829, within the range of ophthalmologist assessments (highest clinician accuracy = 0.767). This DL-based system offers an efficient, objective tool for periorbital assessment and LF grading, supporting personalized diagnosis and surgical planning in blepharoptosis management.

Indexed as

BlepharoptosisDeep LearningEyelidsAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesAutomatic measurementBlepharoptosisDeep learningLevator functionTreatment planning

Identifiers

PMID41634468

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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.