Artificial Intelligence in Aortic Regurgitation: A Multicenter Randomised Controlled Trial
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 540
- 试验地点
- 1
- 主要终点
- Study Outcomes
研究概览
简要总结
This research project aims to develop and validate a tool that uses artificial intelligence (AI) to automatically detect and quantify aortic regurgitation (AR). The clinical efficacy of this tool will be established by comparing it to manual diagnostic methods in a multicenter randomized controlled trial. By leveraging deep learning (DL) techniques, the AI system will automate aortic regurgitation (AR) detection, measurement, and diagnosis, addressing challenges like variability in echocardiographic interpretations and the need for specialized expertise. It will integrate multiple echocardiographic parameters to provide accurate, standardized, and efficient AR diagnoses, reducing human error and improving consistency. This tool will enhance diagnostic precision and accessibility, improving clinical outcomes and extending advanced diagnostic capabilities to a broader range of healthcare environments, including resource-limited settings.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Double (Participant, Investigator)
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Confirmed AR diagnosis via TTE and Doppler imaging per guidelines.
- •Age ≥ 18 years.
- •Adequate acoustic window for AR quantification.
排除标准
- •Prior cardiac transplant or implanted cardiac devices.
- •Poor image quality.
- •Pregnancy or lactation.
结局指标
主要结局
Study Outcomes
时间窗: This will be recorded from baseline to study completion (20 months)
To compare the accuracy of the AI group and the manual group in distinguishing severe from non-severe AR, using expert cardiologists' (ASE level III or equivalent) assessments as the reference standard.
次要结局
- Comparing Accuracy in Differentiating AR Severity Levels(This will be recorded from baseline to study completion (20 months))
- Assessing deviations in Effective Regurgitant Orifice Area (EROA)(This will be recorded from baseline to study completion (20 months))
- Assessing deviations in Vena Contracta (VC)(This will be recorded from baseline to study completion (20 months))
- Assessing deviations in Proximal Isovelocity Surface Area (PISA)(This will be recorded from baseline to study completion (20 months))
- Assessing deviations in jet width(This will be recorded from baseline to study completion (20 months))
- Assessing deviations in Regurgitant Volume (RegVol)(This will be recorded from baseline to study completion (20 months))
- Comparing Assessment Completion Time(The time taken for each method to reach a diagnosis will be recorded from baseline to study completion (20 months))
- Tracking 1-Year Outcomes(Participants will be followed up at 6 and 12 months to monitor outcomes, including 1-year all-cause mortality and HFH.)
研究者
Dr Alex PW Lee
Professor
Chinese University of Hong Kong