five

Anterior Segment Measurement and Reproducibility in Pediatric Eyes Using Quantitative Ultrasound Biomicroscopy

收藏
Taylor & Francis Group2025-08-19 更新2026-04-16 收录
下载链接:
https://tandf.figshare.com/articles/dataset/Anterior_Segment_Measurement_and_Reproducibility_in_Pediatric_Eyes_Using_Quantitative_Ultrasound_Biomicroscopy/29164228/1
下载链接
链接失效反馈
官方服务:
资源简介:
The purpose of this study is to provide an evidence-based protocol for iris measurement from ultrasound biomicroscopy (UBM) images with reproducibility analysis and resulting normative iris thickness dataset of the pediatric human iris by age. Healthy pediatric subjects (14 subjects, 25 eyes, mean age 2.0 ± 1.2 years) were recruited prospectively and underwent UBM imaging. Iris parameters were measured in 4 UBM images per eye in raw image and processed edge detection format. Agreement and variability were evaluated. Regression assessed the association between measurement differences and the variables expected to influence measurement reproducibility (image quality, orientation, and processing). Iris thickness by age was reported. Intraclass correlation was &gt;0.6 and correlation was &gt;0.7 for all parameters. Coefficient of variation was &lt;30% for iris measurements not involving the ciliary body. Image quality improved reproducibility but was not statistically significant (<i>p</i> = 0.10). Age of subject, edge detection, and image orientation were also not significant. Iris thickness increased with increasing age (<i>r</i> = 0.63, <i>p</i> &lt; 0.0001). This study demonstrated reproducible iris measurements using a prospective protocol. We found image features, including image quality and edge detection pre-processing, were not critical to reproducibility. In the future, clinical correlations with iris morphology may be more rigorously studied using a well-defined, reproducible, and quantitative approach as presented in this UBM-based image analysis protocol.
提供机构:
Madigan, William; Miglani, Trisha; Jaafar, Mohamad; Kolosky, Taylor; Vinnett, Alfred; Chang, Michael; Alexander, Janet L.; Wei, Libby; Levin, Moran R.; Bazemore, Marlet; Martinez, Camilo; Shah, Dhruv
创建时间:
2025-05-28
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作