COVID-19 Volumetric Quantification on Computer Tomography Using Computer Aided Diagnostics
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- Mean difference of lung affection quantification percentage
研究概览
简要总结
The aim of the study is to asses the influence of computer aided diagnostic to the process of lung affection quantification on computer tomography in COVID-19 confirmed patients.
详细描述
The lung involvement of COVID-19 patients has been showed to be correlated to clinical outcomes and became part of the clinical practice. Even though various scores can be used, the affection estimation is usually done on computer tomography, using radiologists's estimation skills which is a highly subjective process.
Artificial intelligence is a known objective constant and therefore a potential radiologist complement. This trial aims at studying the effect of using a computer aided diagnostic software integrated in the normal clinical practice of radiologists from Timisoara County Emergency Hospital. It uses the AI-PROBE analysis setup, which turns off the CAD outputs for randomly chosen 50% the cases (control) and then compares the radiological reports for differences between the two arms.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Single (Participant)
盲法说明
The random assignment is done automatically by the CAD system and is not visible to the patient. The radiologist obviously sees which cases have CAD analysis and which not.
入排标准
- 年龄范围
- 16 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •RT-PCR confirmed patients of COVID-19
排除标准
- •15 or lower
结局指标
主要结局
Mean difference of lung affection quantification percentage
时间窗: At CT acquisition time, up to 2 weeks
The objective measurement of lung affection percentage is measured against pixel level labels. A lower difference mean better outcome.
次要结局
未报告次要终点
研究者
Bogdan Bercean
Head of Artificial Intelligence
XVision