AI Assisted Reader Evaluation in Acute CT Head Interpretation
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 33
- 试验地点
- 4
- 主要终点
- Reader performance: Positive and negative predictive value, comparative between with and without AI assistance.
研究概览
简要总结
This study has been added as a sub study to the Simulation Training for Emergency Department Imaging 2 study (ClinicalTrials.gov ID NCT05427838).
The purpose of the study is to assess the impact of an Artificial Intelligence (AI) tool called qER 2.0 EU on the performance of readers, including general radiologists, emergency medicine clinicians, and radiographers, in interpreting non-contrast CT head scans. The study aims to evaluate the changes in accuracy, review time, and diagnostic confidence when using the AI tool. It also seeks to provide evidence on the diagnostic performance of the AI tool and its potential to improve efficiency and patient care in the context of the National Health Service (NHS). The study will use a dataset of 150 CT head scans, including both control cases and abnormal cases with specific abnormalities. The results of this study will inform larger follow-up studies in real-life Emergency Department (ED) settings.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Radiologists/Radiographers/ED clinicians who review CT head scans as part of their clinical practice
排除标准
- •Neuroradiologists.
- •Non-radiologist groups: Clinicians with previous formal postgraduate CT reporting training
- •Emergency Medicine group: Clinicians with previous career in radiology/neurosurgery to registrar level
结局指标
主要结局
Reader performance: Positive and negative predictive value, comparative between with and without AI assistance.
时间窗: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Reader performance will be evaluated as Positive Predictive Value (PPV) and negative predictive value (NPV), with and without AI assistance.
qER (AI algorithm) performance: Positive and negative predictive value.
时间窗: During 6 weeks, which is the period for reading or reviewing the cases/scans.
qER performance will be evaluated as Positive Predictive Value (PPV) and negative predictive value (NPV).
Reader performance: Sensitivity, specificity, comparative between with and without AI assistance.
时间窗: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Reader performance will be evaluated as sensitivity, specificity, with and without AI assistance.
Reader speed: Mean time taken to review a scan, with versus without AI assistance.
时间窗: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Reader speed will be evaluated as the man time taken to review a scan, using time unite of seconds.
Reader confidence: Self-reported diagnostic confidence on a 10 point visual analogue scale, with vs without AI assistance.
时间窗: During 6 weeks, which is the period for reading or reviewing the cases/scans.
On the reading platform (RAIQC), one of the questions asks the level of confidence that the participant has in their diagnostic opinion. The question offers a scale of 1 to 10, where 1 is not confident, and 10 is highly confident.
qER (AI algorithm) performance: Sensitivity and specificity
时间窗: During 6 weeks, which is the period for reading or reviewing the cases/scans.
qER performance will be evaluated as sensitivity, specificity.
Reader performance: Area Under Receiver Operating Characteristic Curve (AUROC), comparative between with and without AI assistance.
时间窗: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Reader performance will be evaluated as Area Under Receiver Operating Characteristic Curve (AUROC), with and without AI assistance.
qER (AI algorithm) performance: Area Under Receiver Operating Characteristic Curve (AUROC).
时间窗: During 6 weeks, which is the period for reading or reviewing the cases/scans.
qER performance will be evaluated as Area Under Receiver Operating Characteristic Curve (AUROC)
次要结局
未报告次要终点
研究者
Alex Novak
Primary Investigator
Oxford University Hospitals NHS Trust