Department of Ophthalmology, Peking University Third Hospital, Beijing, China.
Research Centre of Multiphase Flow in Porous Media, China University of Petroleum (East China), Qingdao, China.
Acta Ophthalmol. 2019 Nov;97(7):e973-e980. doi: 10.1111/aos.14107. Epub 2019 May 2.
To present a new automated image recognition software for the measurement of tear meniscus height (TMH) and investigate its correlation and efficacy compared with an open-source software (NIH ImageJ) and manual evaluation.
A total of 520 slit lamp photographs, among which 276 were in ×16 magnification and 244 were ×40 magnified, captured from 138 eyes of 69 healthy subjects were assessed for TMH by the new automated Tear Meniscus Identification Software (TMIS), ImageJ and human graders. Images processing of TMIS included filtration, recognition and measurement of slit lamp photographs under certain algorithm, which output two measurement patterns, TMIS and TMIS . TMH measured by ImageJ software, considered as the reference value, was conducted by a masked observer while four masked ophthalmologists performed the manual evaluation.
In both magnifications, TMH measured by TMIS showed similar values with ImageJ while manual evaluation demonstrated underestimated results, and a strong correlation was detected between TMIS and ImageJ. In ×16 magnified photographs, manually obtained TMH revealed a higher correlation with ImageJ, whereas a notably stronger correlation of TMIS with ImageJ was observed in ×40 photographs. Correspondingly, the accuracy for both TMIS and TMIS appeared to be lower than most doctors in ×16 slit lamp images, in contrast to a better precision of TMIS in ×40 ones.
The new software displayed high accuracy and efficacy in ×40 magnification and TMIS pattern, suggesting the possibility of this automated TMH measurement platform to be a valid tool in dry eye screening and follow-up practice.
介绍一种新的自动化泪膜月牙高度（TMH）图像识别软件，并与开源软件（NIH ImageJ）和手动评估进行对比，研究其相关性和有效性。
共评估了 138 只眼 69 名健康受试者的 520 张裂隙灯照片，其中 276 张放大倍数为×16，244 张放大倍数为×40。新的自动化泪膜月牙识别软件（TMIS）、ImageJ 和人工评分者分别对 TMH 进行评估。TMIS 的图像处理包括在特定算法下对裂隙灯照片进行滤波、识别和测量，输出两种测量模式，TMIS 和 TMIS 。ImageJ 软件测量的 TMH 被认为是参考值，由一名掩蔽观察者进行，而四名掩蔽眼科医生进行手动评估。
在两种放大倍数下，TMIS 测量的 TMH 值与 ImageJ 相似，而手动评估结果显示低估，TMIS 与 ImageJ 之间存在很强的相关性。在×16 放大倍数的照片中，手动获得的 TMH 与 ImageJ 相关性更高，而在×40 照片中，TMIS 与 ImageJ 的相关性更强。相应地，在×16 裂隙灯图像中，TMIS 和 TMIS 的准确性似乎低于大多数医生，但在×40 图像中，TMIS 的精度更高。
新软件在×40 放大倍数和 TMIS 模式下显示出较高的准确性和有效性，表明该自动化 TMH 测量平台有可能成为干眼症筛查和随访实践的有效工具。
