NCT05471869Unknown不适用
Computational Imaging Using Pixel-level Graph Adversarial Learning
Chinese PLA General Hospital1 个研究点 分布在 1 个国家目标入组 1,200 人开始时间: 2021年11月1日最近更新:
适应症
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 1,200
- 试验地点
- 1
- 主要终点
- Objective evaluation
研究概览
简要总结
Computational imaging research based on deep learning
详细描述
Based on the current technical challenges, subject development and upgrade of knowledge, to avoid the occurrence of adverse medical accidents, simplify the diagnostic process, artificial intelligence has become the alternative method of choice, by constructing training deep learning model, the CTA as model inputs aneurysm detection and diagnosis to improve diagnosis effectiveness, promote the development of medical technology
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Age: 18-80 years.
- •CT paired imaging data of vessels, including layer-to-layer plain CT and enhanced CT. 2 Time range: January 2010 to December
- •Scanning sites: CT and CTA of head, neck, chest, abdomen or iliac.
排除标准
- •Unpaired CT image .
- •Severe artifact CT image.
- •enhancement CT failure image (failure to capture arterial phase or poor arterial development, insufficient flow of contrast agent, etc.)
结局指标
主要结局
Objective evaluation
时间窗: 30 minutes
using SSIM\\MAE index evaluation
次要结局
未报告次要终点
研究者
Xin Lou
professor
Chinese PLA General Hospital
研究点 (1)
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