Artificial Intelligence-Based Assessment of Low-Gradient Aortic Stenosis Severity Using Echocardiographic Images
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 300
- 试验地点
- 1
- 主要终点
- Area Under the Receiver Operating Characteristic Curve (AUC) describing the sensitivity-specificity relationship of the AI model.
研究概览
简要总结
The purpose of this study is to evaluate the effectiveness of an artificial intelligence (AI) model developed by the investigators for identifying severe low-gradient aortic valve stenosis. Accurate assessment of stenosis severity is crucial for proper qualification for surgical treatment. It is expected that the use of AI will improve diagnostic accuracy and thereby support better clinical outcomes.
Patients with suspected significant low-gradient aortic stenosis will be enrolled. The study is observational and involves no additional risk for participants. Standard imaging studies performed for clinical indications will be additionally analyzed by the AI model, which will classify aortic stenosis as severe or moderate. The model's results will not influence the clinical management of participants but will be compared with physicians' assessments to validate its diagnostic performance.
The study will be conducted in 2025-2026. The findings will provide insights into the usefulness of AI in the diagnosis of severe aortic stenosis and may contribute to the development of advanced clinical decision-support tools.
详细描述
This study is a prospective multicenter observational validation of an artificial intelligence (AI) model for differentiating severe low-gradient from moderate aortic stenosis using transthoracic echocardiography images. The model, developed and published by the investigators, demonstrated promising diagnostic performance in retrospective data. In the present trial, approximately 300 participants with suspected significant low-gradient aortic stenosis will be enrolled during 2025-2026. Standard imaging studies performed for clinical indications will be analyzed by the AI model, which will classify aortic stenosis as severe or moderate. The AI-derived results will not influence clinical decision-making but will be compared with physicians assessments to evaluate diagnostic accuracy and reproducibility in real-world practice.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
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
- •Age ≥ 18 years
- •Clinical suspicion of significant low-gradient aortic stenosis
- •Echocardiographic examination performed for clinical indications
- •Ability to provide informed consent
排除标准
- •Previous aortic valve intervention (surgical or transcatheter)
- •Inadequate image quality precluding echocardiographic analysis
- •Concomitant severe valvular disease (severe mitral stenosis or mitral/aortic regurgitation) that could confound assessment
- •Patients unwilling or unable to provide informed consent
结局指标
主要结局
Area Under the Receiver Operating Characteristic Curve (AUC) describing the sensitivity-specificity relationship of the AI model.
时间窗: At the time of the nearest Heart Team meeting following the echocardiographic examination (typically within 1 week).
AUC will be calculated to assess the ability of the AI model to differentiate between severe low-gradient and moderate aortic stenosis. The analysis will use physician assessment and guideline-based diagnostic criteria as the reference standard. AUC will be reported with 95% confidence intervals.
次要结局
- Diagnostic performance of the AI model in clinically relevant subgroups.(At the nearest Heart Team meeting following the echocardiographic examination (typically within 1 week).)