Lung CT Scan Analysis of SARS-CoV2 Induced Lung Injury by Machine Learning: a Multicenter Retrospective Cohort Study.
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 44
- 试验地点
- 8
- 主要终点
- A qualitative analysis of parenchymal lung damage induced by COVID-19
研究概览
简要总结
This is a multicenter observational retrospective cohort study that aims to study the morphological characteristics of the lung parenchyma of SARS-CoV2 positive patients identifiable in patterns through artificial intelligence techniques and their impact on patient outcome.
详细描述
BACKGROUND:
In February, the first case of SARS-CoV2 positive patient was recorded in Lombardy (Italy), a virus capable of causing a severe form of acute respiratory failure called Coronavirus Disease 2019 (COVID-19).
Qualitative assessments of lung morphology have been identified to describe macroscopic characteristics of this infection upon admission and during the hospitalization of patients.
At the moment, there are no studies that have exhaustively described the parenchymal lung damage induced by SARS-CoV2 by quantitative analysis.
The hypothesis of this study is that specific morphological and quantitative alterations of the lung parenchyma assessed by means of CT scan in patients suffering from severe respiratory insufficiency induced by SARS-CoV2 may have an impact on the severity of the degree of alteration of the respiratory exchanges (oxygenation and clearance of the CO2) and have an impact on patient outcome.
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 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- 未提供
排除标准
- 未提供
结局指标
主要结局
A qualitative analysis of parenchymal lung damage induced by COVID-19
时间窗: Until patient discharge from the hospital (approximately 6 months)
Describe the parenchymal lung damage induced by COVID-19 through a qualitative analysis with chest CT through artificial intelligence techniques.
A quantitative analysis of parenchymal lung damage induced by COVID-19
时间窗: Until patient discharge from the hospital (approximately 6 months)
Describe the parenchymal lung damage induced by COVID-19 through a quantitative analysis with chest CT through artificial intelligence techniques.
次要结局
- The potential impact of parenchymal morphological CT scans in patients with severe moderate respiratory failure.(Until patient discharge from the hospital (approximately 6 months))
- Automated segmentation of lung scans of patients with COVID-19 and ARDS.(Until patient discharge from the hospital (approximately 6 months))
- Knowledge of chest CT features in COVID-19 patients and their detail through the use of machine learning and other quantitative techniques.(Until patient discharge from the hospital (approximately 6 months))
- The ability within which the analysis of artificial intelligence that uses deep learning models can be used to predict clinical outcomes(Until patient discharge from the hospital (approximately 6 months))