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NCT Number: NCT06768398

AI-Based IMT Study

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.

Recruiting

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Key information

Age range

19 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

The Chinese University of Hong Kong

Shatin, 999077, Hong Kong

Location status: Recruiting

Location contact

Daniel Xu

CONTACT

[email protected]

35051518 ext. 1518

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. ultrasound images of their carotid artery

    Time frame: 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 outcomes

  1. AI deep learning model

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Chinese University of Hong Kong

Other

Registry information

Official study title

AI-Based Intima-Media Thickness Measurement for Cardiovascular Risk Assessment

Important dates

Study start
2024
Primary completion
2024
Study completion
2025
First posted
Jan 10, 2025
Registry last updated
Jan 10, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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