Artificial Intelligence to Improve Chest X-ray Reading in Acute Dyspnoeic Patients: A Randomized Controlled Trial
试验速览
- 阶段
- 不适用
- 入组人数
- 33
- 试验地点
- 1
- 主要终点
- Accuracy of diagnosing ADHF on acute CXR with vs without AI
研究概览
简要总结
Identifying the cause of breathlessness in acute patients in the emergency department is critical and challenging. The chest X-ray is central but challenging to read for non-radiologist physicians. Often the physicians read the CXR alone due to off-hours and shortage of radiology specialists. Artificial Intelligence (AI) has the potential to aid the reading of chest X-rays. The hypothesis is that AI applied to chest X-rays improves emergency physicians' diagnostic accuracy in acute breathless patients.
详细描述
Background:
Acute dyspnoea is a common symptom in the emergency department (ED) but possible differential diagnoses are numerous. The chest X-ray (CXR) is of great importance in distinguishing between these diagnoses and initiating proper treatment but is challenging to interpret for non-radiologist physicians. Radiology departments are confronted with a demand to read a constantly increasing number of acutely performed CXRs, which exceeds the necessary resources. Therefore, in the acute setting, emergency physicians must often read and diagnose the CXR alone. Altogether, there is an unmet need for help with the CXR interpretation in the ED.
Artificial intelligence (AI) software for interpreting CXR has been developed for the detection of pathological findings. In this study, the primary aim is to investigate if AI improves the diagnosis on CXR by non-radiologist physicians in consecutive dyspnoeic patients in the emergency department.
The investigators hypothesize, that AI applied to chest X-rays improves the emergency physicians' diagnostic accuracy in acute dyspnoeic patients. The study has the potential to impact the implementation of AI in clinical practice.
Method:
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Diagnostic
- 盲法
- None
盲法说明
Allocation of images was performed before inclusion of participants began. Allocation process ensured that is was unnecessary for the investigator to assess the randomization.
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Medical Doctor (MD)
- •Working experience with emergency patients
排除标准
- •Current or former employment as a radiologist
- •Unwillingness to consent
结局指标
主要结局
Accuracy of diagnosing ADHF on acute CXR with vs without AI
时间窗: 3 months
The primary outcome is the difference in diagnostic accuracy of the non-radiologist physicians' diagnosis of ADHF on acute CXR compared with the gold standard. Odds of correct diagnosis are compared using an odds ratio with 95% confidence interval estimated using conditional logistic regression stratified by each image with and without AI. Thus, the improvement in the odds of correct classification after versus before AI support is reported. The significance level is 0.025.
Accuracy of diagnosing pneumonia on acute CXR with vs without AI
时间窗: 3 months
The primary outcome is the difference in diagnostic accuracy of the non-radiologist physicians' diagnosis of pneumonia on acute CXR compared with the gold standard. Odds of correct diagnosis are compared using an odds ratio with 95% confidence interval estimated using conditional logistic regression stratified by each image with and without AI. Thus, the improvement in the odds of correct classification after versus before AI support is reported. The significance level is 0.025.
次要结局
未报告次要终点
研究者
Olav Wendelboe Nielsen
Clinical professor at University of Copenhagen, MD, PhD
Bispebjerg Hospital