Cardiovascular Diseases
Conditions
Brief summary
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.
Interventions
None listed
Sponsors
Chinese University of Hong Kong
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
19 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* none
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| ultrasound images of their carotid artery | 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.) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| AI deep learning model | 1 day | The collected ultrasound image data is a part of where the AI deep learning model will base on. Upon training of the convolutional neural network, the model will classify the input ultrasound images into the three risk categories, which serves as a preventive healthcare to cardiovascular diseases. |
Countries
Hong Kong
Outcome results
None listed