A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery

Ziying Zhou; Shengqiang Shi; Xiajing Tang; Zhaoyang Xu; Juan Ye; Xingru Huang; Lixia Lou

doi:10.1016/j.aopr.2024.06.004

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A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery

Full Length Article | 更新时间：2024-12-14

- A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery
- AOPR Vol. 4, Issue 4, Pages: 202-208(2024)
- 作者机构：
  
  1. Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases,Hangzhou,China
  2. Hangzhou Dianzi University,Hangzhou,China
  3. Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
  4. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
- 作者简介：
- 基金信息：
- DOI：10.1016/j.aopr.2024.06.004
  CLC：
- Published：2024，
- 稿件说明：
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ZIYING ZHOU, SHENGQIANG SHI, XIAJING TANG, et al. A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery. [J]. Aopr, 2024, 4(4): 202-208.
DOI：

ZIYING ZHOU, SHENGQIANG SHI, XIAJING TANG, et al. A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery. [J]. Aopr, 2024, 4(4): 202-208. DOI： 10.1016/j.aopr.2024.06.004.

摘要

Abstract

PurposeThis study aimed to propose a novel deep learning-based approach to assess the extent of abduction in patients with abducens nerve palsy before and after strabismus surgery.MethodsThis study included 13 patients who were diagnosed with abducens nerve palsy and underwent strabismus surgery in a tertiary hospital. Photographs of primary

dextroversion and levoversion position were collected before and after strabismus surgery. The eye location and eye segmentation network were trained via recurrent residual convolutional neural networks with attention gate connection based on U-Net (R2AU-Net). Facial images of abducens nerve palsy patients were used as the test set and parameters were measured automatically based on the masked images. Absolute abduction also was measured manually

and relative abduction was calculated. Agreements between manual and automatic measurements

as well as repeated automatic measurements were analyzed. Preoperative and postoperative results were compared.ResultsThe intraclass correlation coefficients (ICCs) between manual and automatic measurements of absolute abduction

ranged from 0.985 to 0.992 (

＜0.001)

and the bias ranged from −0.25 mm to −0.05 mm. The ICCs between two repeated automatic measurements ranged from 0.994 to 0.997 (

＜0.001)

and the bias ranged from −0.11 mm to 0.05 mm. After strabismus surgery

absolute abduction of affected eye increased from 2.18 ± 1.40 mm to 3.36 ± 1.93 mm (

＜0.05). The relative abduction was improved in 76.9% patients (10/13) after surgery (

＜0.01).ConclusionsThis image analysis technique demonstrated excellent accuracy and repeatability for automatic measurements of ocular abduction

which has promising application prospects in objectively assessing surgical outcomes in patients with abducens nerve palsy.

关键词

Keywords

Abducens nerve palsyDeep learningAutomatic image analysisSurgery

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