Evaluation of a Chest X-Ray AI Neural Network (RadGen SARS-CoV2 Detection System) for the Detection of RT-PCR Confirmed SARS-Cov2 Covid-19 Pneumonia
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 4,000
- 试验地点
- 1
- 主要终点
- Diagnostic Performance of AI model
研究概览
简要总结
This study investigates the diagnostic performance of an AI algorithm in the detection of COVID-19 pneumonia on chest radiographs.
详细描述
This is an international multi-center study. Chest radiographs (CXR) from different participating centers will be collected to develop an AI algorithm to detect COVID-19 pneumonia. This will be tested on external hold out datasets from different centers using SARS-CoV-2 by Real-Time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) Assay as ground truth.
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 120 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •All adult patients >18 years of age
- •Attended any of the participating institutes between February 1, 2020 until September, 2020
- •Underwent both RT-PCR testing and frontal CXR (within 48 hours of PCR testing) for COVID-19 infection
- •frontal CXR of patients pre-covid pandemic
排除标准
- •Unavailability of patient demographics and clinical data
- •Inconclusive RT-PCR results
- •CXR considered to be of non-diagnostic quality by the clinical radiology research team at each site
- •CXR not in a retrievable or processable format for AI inference
结局指标
主要结局
Diagnostic Performance of AI model
时间窗: 9 months
Performance (accuracy, sensitivity, specificity, false-positive rate (FPR), false-negative rate (FNR), and Area Under the Curve (AUC)) of the AI model in detection of COVID-19 pneumonia on their baseline CXR using RT-PCR and historical controls as gold standard in a multi-center / multi-national cohort.
次要结局
未报告次要终点