WeChat Mini Program
Old Version Features

应用光学相干断层扫描血管成像评估视网膜血流的可重复性及再现性

Eye Science(2021)

Cited 1|Views15
Abstract
目的:应用Cirrus HD-OCT 5000对正常眼黄斑及视盘血流参数进行血管成像测量,评估其可重复性及再现性.方法:纳入40只正常眼进行前瞻性研究.操作者A于一周内3天的同一时段(T1,T2,T3)对正常受试者的同一眼黄斑及视盘进行3次血管成像扫描,扫描过程中均开启FastTracTM图像跟踪功能,操作者B在T2时间点再次对受试者同一眼进行相同程序扫描,使用Angio Plex MetrixTM量化软件(版本10.0)自动测量黄斑和视盘的血管长度密度(vessel length density,VD)和血管灌注密度(vascular perfusion density,PD).应用单因素方差分析或非参数检验比较3次扫描的VD、PD是否有差异.采用组内相关系数(intraclass correlation coefficient,ICC)、变异系数(coefficient of variation,CV)评价操作者A的可重复性,采用一致性相关系数(consistent correlation coefficient,CCC)、重复性系数(repeatability coefficient,CR)、CV评价操作者间的再现性.结果:操作者A对黄斑及视盘各区域3次扫描的VD、PD之间差异无统计学意义(P>0.05).操作者A对黄斑和视盘VD、PD 3次扫描的ICC值分别为0.260~0.517、0.362~0.898,黄斑及视盘各区域VD、PD的CV值均<8.1%.黄斑和视盘VD、PD的操作者间CCC值分别为0.3130~0.5665、0.5149~0.7801;黄斑VD和PD的CR值分别为3.2212~4.6399、0.0574~0.0832;视盘VD和PD的CR值分别为2.0675~4.0630、0.0447~0.0730.黄斑CV值均<9.0%,视盘CV值均<6.9%.结论:非同日的同一时段视盘浅层血流参数具有较好的重复性及再现性,黄斑浅层血流参数的重复性及再现性相对较差.
More
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined