Utility of an AI-based CXR Interpretation Tool in Assisting Diagnostic Accuracy, Speed, and Confidence of Healthcare Professionals: a Study Using 500 Retrospectively Collected Inpatient and Emergency Department CXRs From Two UK Hospital Trusts
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 33
- 试验地点
- 1
- 主要终点
- Performance of AI algorithm: sensitivity
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 Lunit INSIGHT CXR is a validation study that aims to assess the utility of an Artificial Intelligence-based (AI) chest X-ray (CXR) interpretation tool in assisting the diagnostic accuracy, speed, and confidence of a varied group of healthcare professionals. The study will be conducted using 500 retrospectively collected inpatient and emergency department CXRs from two United Kingdom (UK) hospital trusts. Two fellowship trained thoracic radiologists will independently review all studies to establish the ground truth reference standard. The Lunit INSIGHT CXR tool will be used to analyze each CXR, and its performance will be measured against the expert readers. The study will evaluate the utility of the algorithm in improving reader accuracy and confidence as measured by sensitivity, specificity, positive predictive value, and negative predictive value. The study will measure the performance of the algorithm against ten abnormal findings, including pulmonary nodules/mass, consolidation, pneumothorax, atelectasis, calcification, cardiomegaly, fibrosis, mediastinal widening, pleural effusion, and pneumoperitoneum. The study will involve readers from various clinical professional groups with and without the assistance of Lunit INSIGHT CXR. The study will provide evidence on the impact of AI algorithms in assisting healthcare professionals such as emergency medicine and general medicine physicians who regularly review images in their daily practice.
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
- •General radiologists/radiographers/physicians who review CXRs as part of their routine clinical practice
排除标准
- •Thoracic radiologists
- •Non-radiology physicians with previous formal postgraduate CXR reporting training.
- •Non-radiology physicians with previous career in radiology, respiratory medicine or thoracic surgery to registrar or consultant level
结局指标
主要结局
Performance of AI algorithm: sensitivity
时间窗: During 4 weeks of reading time
Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard in order to determine sensitivity.
Performance of AI algorithm: specificity
时间窗: During 4 weeks of reading time
Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard in order to determine specificity.
Performance of readers with and without AI assistance: Sensitivity
时间窗: During 4 weeks of reading time
The study will include two sessions (with and without AI overlay), with all 30 readers reviewing all 500 CXR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader.
Performance of readers with and without AI assistance: Area under the ROC Curve (AU ROC)
时间窗: During 4 weeks of reading time
The study will include two sessions (with and without AI overlay), with all 30 readers reviewing all 500 CXR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader.
Performance of AI algorithm: Area under the ROC Curve (AU ROC)
时间窗: During 4 weeks of reading time
Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard. Continuous probability score from the algorithm will be utilized for the ROC analyses, while binary classification results with a predefined operating cut-off will be used for evaluation of sensitivity, specificity, positive predictive value, and negative predictive value.
Reader speed with vs without AI assistance.
时间窗: During 4 weeks of reading time
Mean time taken to review a scan, with vs without AI assistance.
Performance of readers with and without AI assistance: Specificity
时间窗: During 4 weeks of reading time
The study will include two sessions (with and without AI overlay), with all 30 readers reviewing all 500 CXR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader.
次要结局
未报告次要终点
研究者
Alex Novak
Primary Investigator
Oxford University Hospitals NHS Trust