AI-Based Intima-Media Thickness Measurement for Cardiovascular Risk Assessment
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 80
- 试验地点
- 1
- 主要终点
- ultrasound images of their carotid artery
研究概览
简要总结
Cerebro-vascular and heart diseases have together ranked 4th and 5th place in the 2022 top ten leading causes of death in Hong Kong, taking up more than 15% of the total in an unceasing trend. While conventional carotid ultrasound imaging is nothing short of comprehensive, it is highly operator-dependent and is worsened by the shortage of medical staff in Hong Kong.
The seemingly long queue for the expensive health screenings has put the high-risk groups, including but not limited to the elderly, in a vulnerable position as they can hardly perform regular and frequent check-ups.
In light of this, our team is determined to research a solution that is conducive to the preventive healthcare of strokes and cardiovascular diseases through one of the newly proposed devices: PyrocksTM Tag Lite.
This study aims to investigate an approach for developing a robust deep learning model for analysing ultrasound images and incorporate the model into our established prototype to perform intima-media thickness measurement and risk assessment.
Main points that the clinical trial can assist in solving the existing problem:
The acquisition procedures are non-invasive, painless, and safe for the participants. Clinical trials & test data will assist in testing and training our neural network model.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Prospective
入排标准
- 年龄范围
- 19 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adults (over the age of 18 years)(with Elderlies (over the age of 65 years) more preferred)
- •Patients with cardiovascular diseases (CVD), including current smokers or diagnosed with diabetes, dyslipidaemia, coronary artery disease, cerebrovascular disease, hypertension, atherosclerotic cardiovascular disease, high blood pressure, high BMI index and those under antihypertensive treatment.
排除标准
- 未提供
结局指标
主要结局
ultrasound images of their carotid artery
时间窗: 1 day
For each human participant, we will collect at least 100 ultrasound images of their carotid artery. In total, there will be approximately 80x100=8000 ultrasound images. From the ultrasound images, we will measure the thickness of the participants' carotid artery wall and assess their cardiovascular risk according to risk charts (if \>1mm: low risk; if \>1mm \& \<2.5mm: intermediate risk; if \>2.5mm: high risk.)
次要结局
- AI deep learning model(1 day)
研究者
Professor Bryan Ping Yen YAN
professor
Chinese University of Hong Kong