Artificial Intelligence to Assess the Association Between Multi-dimension Facial Characteristics and Coronary Artery Diseases
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 460
- 试验地点
- 1
- 主要终点
- Area under receiver operating curve (AUC)
研究概览
简要总结
The purposes of this study are 1) to explore the association between multi-dimension facial characteristics and the increased risk of coronary artery diseases (CAD); 2) to evaluate the diagnostic efficacy of multi-dimension appearance factors for coronary artery diseases.
详细描述
Previous study demonstrated the feasibility of using deep learning to detect coronary artery disease based on facial photos. However, several limitations made the algorithm hard to be utilized in clinical practice, including low specificity and lack of external validation. Adding multi-dimension facial characteristics may further increase the algorithm effect.
Thus, the investigators designed a single-center, cross-sectional study to explore the association between multi-dimension facial characteristics and CAD and to evaluate the predictive efficacy of multi-dimension appearance factors for CAD. The investigators will recruit patients undergoing coronary angiography or coronary computer tomography angiography. Patients' baseline information and multi-dimension facial images will be collected. The investigators will train and validate a deep learning algorithm based on multi-dimension facial photos.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 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
- 是
入选标准
- •Undergoing coronary angiography or coronary computer tomography angiography
- •Written informed consent
排除标准
- •Prior percutaneous coronary intervention (PCI)
- •Prior coronary artery bypass graft (CABG)
- •Screening coronary artery disease before treating other heart diseases
- •Without blood biochemistry outcome
- •With artificially facial alteration (i.e. cosmetic surgery, facial trauma or make-up)
- •Other situations which make patients fail to be photographed
结局指标
主要结局
Area under receiver operating curve (AUC)
时间窗: At the end of enrollment (1 mouth)
Area under receiver operating curve of algorithm assessed in test group
次要结局
- Sensitivity of algorithm(At the end of enrollment (1 mouth))
- Specificity of algorithm(At the end of enrollment (1 mouth))
- Positive predictive value (PPV)(At the end of enrollment (1 mouth))
- Negative predictive value (NPV)(At the end of enrollment (1 mouth))
- Diagnostic accuracy rate(At the end of enrollment (1 mouth))