Literature DB >> 31148484

A Novel Approach for Automated Eyelid Measurements in Blepharoptosis Using Digital Image Analysis.

Lixia Lou1, Longzhao Yang2, Xin Ye1, Yan Zhu3, Shaoze Wang4, Lingling Sun2, Dahong Qian5, Juan Ye1.   

Abstract

Purpose: To propose a novel approach for automated measurements of margin reflex distance (MRD) using digital image analysis and to evaluate the agreement between automated and manual measurements of MRD. Materials and
Methods: This observational study included 132 eyes of 66 volunteers referred to an oculoplastic clinic for blepharoptosis. Preoperative facial photographs of participants were taken. MRD1 and MRD2 were measured manually by a senior surgeon and automatically by our image-based algorithm. Correlation analyses and Bland-Altman analyses were performed to evaluate the agreement between the two measurements.
Results: A strong positive correlation was observed between automated and manual MRD1 measurements, with Spearman's r = 0.968 (95% confidence interval [CI] = 0.949-0.976; P < .001) and intraclass correlation coefficient (ICC) = 0.966 (95% CI = 0.953-0.976; P < .001). The bias between automated and manual MRD1 measurements was 0.02 mm (95% CI = -0.06-0.10 mm), with 95% limits of agreement (LoA) = -0.88 to 0.92 mm. Agreement also existed in MRD2 measurements, with Spearman's r = 0.803 (95% CI = 0.716-0.865; P < .001), ICC = 0.833 (95% CI = 0.772-0.879; P < .001), and bias = 0.34 mm (95% CI = 0.26-0.42 mm), 95% LoA = -0.54 to 1.22 mm. Conclusions: Automated eyelid measurements in blepharoptosis using the image-based approach compare favorably with clinical manual measurements. This novel approach allows an objective assessment of ptosis with high accuracy.

Entities:  

Keywords:  Blepharoptosis; automated measurement; image analysis; margin reflex distance

Year:  2019        PMID: 31148484     DOI: 10.1080/02713683.2019.1619779

Source DB:  PubMed          Journal:  Curr Eye Res        ISSN: 0271-3683            Impact factor:   2.424


  4 in total

1.  An Outperforming Artificial Intelligence Model to Identify Referable Blepharoptosis for General Practitioners.

Authors:  Ju-Yi Hung; Ke-Wei Chen; Chandrashan Perera; Hsu-Kuang Chiu; Cherng-Ru Hsu; David Myung; An-Chun Luo; Chiou-Shann Fuh; Shu-Lang Liao; Andrea Lora Kossler
Journal:  J Pers Med       Date:  2022-02-15

2.  PeriorbitAI: Artificial Intelligence Automation of Eyelid and Periorbital Measurements.

Authors:  Alexandra Van Brummen; Julia P Owen; Theodore Spaide; Colin Froines; Randy Lu; Megan Lacy; Marian Blazes; Emily Li; Cecilia S Lee; Aaron Y Lee; Matthew Zhang
Journal:  Am J Ophthalmol       Date:  2021-05-16       Impact factor: 5.258

3.  Deep learning-based image analysis for automated measurement of eyelid morphology before and after blepharoptosis surgery.

Authors:  Lixia Lou; Jing Cao; Yaqi Wang; Zhiyuan Gao; Kai Jin; Zhaoyang Xu; Qianni Zhang; Xingru Huang; Juan Ye
Journal:  Ann Med       Date:  2021-12       Impact factor: 4.709

4.  A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning.

Authors:  Yiming Sun; Xingru Huang; Qianni Zhang; Sang Yeul Lee; Yaqi Wang; Kai Jin; Lixia Lou; Juan Ye
Journal:  Ophthalmol Sci       Date:  2022-05-18
  4 in total

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