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基于深度学习的甲状腺相关性眼病眼睑形态图像分析

Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy.

作者信息

Shao Ji, Huang Xingru, Gao Tao, Cao Jing, Wang Yaqi, Zhang Qianni, Lou Lixia, Ye Juan

机构信息

Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.

School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.

出版信息

Quant Imaging Med Surg. 2023 Mar 1;13(3):1592-1604. doi: 10.21037/qims-22-551. Epub 2023 Jan 3.

Abstract

BACKGROUND

We aimed to propose a deep learning-based approach to automatically measure eyelid morphology in patients with thyroid-associated ophthalmopathy (TAO).

METHODS

This prospective study consecutively included 74 eyes of patients with TAO and 74 eyes of healthy volunteers visiting the ophthalmology department in a tertiary hospital. Patients diagnosed as TAO and healthy volunteers who were age- and gender-matched met the eligibility criteria for recruitment. Facial images were taken under the same light conditions. Comprehensive eyelid morphological parameters, such as palpebral fissure (PF) length, margin reflex distance (MRD), eyelid retraction distance, eyelid length, scleral area, and mid-pupil lid distance (MPLD), were automatically calculated using our deep learning-based analysis system. MRD1 and 2 were manually measured. Bland-Altman plots and intraclass correlation coefficients (ICCs) were performed to assess the agreement between automatic and manual measurements of MRDs. The asymmetry of the eyelid contour was analyzed using the temporal: nasal ratio of the MPLD. All eyelid features were compared between TAO eyes and control eyes using the independent samples -test.

RESULTS

A strong agreement between automatic and manual measurement was indicated. Biases of MRDs in TAO eyes and control eyes ranged from -0.01 mm [95% limits of agreement (LoA): -0.64 to 0.63 mm] to 0.09 mm (LoA: -0.46 to 0.63 mm). ICCs ranged from 0.932 to 0.980 (P<0.001). Eyelid features were significantly different in TAO eyes and control eyes, including MRD1 (4.82±1.59 2.99±0.81 mm; P<0.001), MRD2 (5.89±1.16 5.47±0.73 mm; P=0.009), upper eyelid length (UEL) (27.73±4.49 25.42±4.35 mm; P=0.002), lower eyelid length (LEL) (31.51±4.59 26.34±4.72 mm; P<0.001), and total scleral area (SA) (96.14±34.38 56.91±14.97 mm; P<0.001). The MPLDs at all angles showed significant differences in the 2 groups of eyes (P=0.008 at temporal 180°; P<0.001 at other angles). The greatest temporal-nasal asymmetry appeared at 75° apart from the midline in TAO eyes.

CONCLUSIONS

Our proposed system allowed automatic, comprehensive, and objective measurement of eyelid morphology by only using facial images, which has potential application prospects in TAO. Future work with a large sample of patients that contains different TAO subsets is warranted.

摘要

背景

我们旨在提出一种基于深度学习的方法,以自动测量甲状腺相关眼病(TAO)患者的眼睑形态。

方法

这项前瞻性研究连续纳入了一家三级医院眼科就诊的74例TAO患者的74只眼和74名健康志愿者的74只眼。诊断为TAO的患者以及年龄和性别匹配的健康志愿者符合纳入标准。在相同光照条件下拍摄面部图像。使用我们基于深度学习的分析系统自动计算睑裂(PF)长度、边缘反射距离(MRD)、眼睑退缩距离、眼睑长度、巩膜面积和瞳孔中点睑距离(MPLD)等综合眼睑形态参数。MRD1和MRD2通过手动测量。采用Bland-Altman图和组内相关系数(ICC)评估MRD自动测量与手动测量之间的一致性。使用MPLD的颞侧:鼻侧比值分析眼睑轮廓的不对称性。使用独立样本t检验比较TAO眼和对照眼之间的所有眼睑特征。

结果

表明自动测量与手动测量之间具有高度一致性。TAO眼和对照眼中MRD的偏差范围为-0.01毫米[95%一致性界限(LoA):-0.64至0.63毫米]至0.09毫米(LoA:-0.46至0.63毫米)。ICC范围为0.932至0.980(P<0.001)。TAO眼和对照眼的眼睑特征存在显著差异,包括MRD1(4.82±1.59对2.99±0.81毫米;P<0.001)、MRD2(5.89±1.16对5.47±0.73毫米;P=0.009)、上睑长度(UEL)(27.73±4.49对25.42±4.35毫米;P=0.002)、下睑长度(LEL)(31.51±4.59对26.34±4.72毫米;P<0.001)和总巩膜面积(SA)(96.14±34.38对56.91±14.97毫米;P<0.001)。两组眼中所有角度的MPLD均存在显著差异(颞侧180°时P=0.008;其他角度时P<0.001)。TAO眼中颞侧与鼻侧最大不对称出现在距中线75°处。

结论

我们提出的系统仅使用面部图像就能自动、全面且客观地测量眼睑形态,在TAO中具有潜在应用前景。未来有必要对包含不同TAO亚组的大量患者样本开展研究。

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