Performance Evaluation of Artificial Intelligence Screening Model in Coronary Heart Disease Detection
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 900
- 试验地点
- 3
- 主要终点
- Diagnostic Accuracy of Participants with Obstructive Coronary Heart Disease
研究概览
简要总结
To determine whether an integrated AI decision support can save time and improve accuracy of assessment of obstructive coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided measurements of obstructive CHD probability compared to clinical assessment in preliminary evaluations by physicians.
详细描述
This is a randomized controlled trial (RCT) evaluating the effectiveness of an AI-based decision support tool in the preliminary assessment of obstructive CHD by physicians. Retrospectively collected medical records of participants with chest pain or dyspnea will be randomly assigned to either guideline group or AI group after baseline assessment:
There are three settings:
- Clinical Intuition (baseline assessment) Physicians assess obstructive CHD probability without any external assistance. Assessment relies solely on the physician's clinical judgment and experience.
- Guideline-Based Group (Guideline Group) Physicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD.
This approach aligns with current clinical guidelines to assist in decision-making. 3. AI-Assisted Group (AI Group) Physicians receive CHD probability estimates and diagnostic recommendations from an AI model based on retinal photographs.
The AI tool provides individualized obstructive CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Screening
- 盲法
- Single (Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 75 Years（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
Diagnostic Accuracy of Participants with Obstructive Coronary Heart Disease
时间窗: Through study completion, an average of 1 week
Whether AI-guided decision support improves the diagnostic accuracy of obstructive coronary heart disease (CHD) to a greater extent than standard clinical assessments (RF-CL), both compared to clinical intuition. All participants of the case records had underwent CT angiography or invasive angiography. The diagnostic accuracy, sensitivity and specificity will be compared across groups.
次要结局
- Time Consumed by Physician Readers to Provide the Diagnosis Impression of Obstructive Coronary Heart Disease.(Through study completion, an average of 1 week)
研究者
Tien Yin Wong
Professor
Tsinghua University