Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study.
Shuang Leng, Nicholas Cheng, Eddy Tan...
https://pubmed.ncbi.nlm.nih.gov/41267847Actively Recruiting
Age: 21Years - 80Years
All Genders
ID05509010
AI Driven National Platform for CT Coronary Angiography for Clinical and Industrial Applications Registry (APOLLO)
Led by National Heart Centre Singapore · Updated on 2022-08-19
8000
Participants Needed
3
Research Sites
N/A
Total Duration
On this page
Sponsors
N
National Heart Centre Singapore
Lead Sponsor
A
Agency for Science, Technology and Research
Collaborating Sponsor
AI-Summary
What this Trial Is About
Coronary artery disease (CAD) is a common condition that affects blood vessels and is a leading cause of death worldwide, including in Singapore. This research aims to develop an AI-driven national platform called APOLLO to improve the use of Computed Tomography Coronary Angiography (CTCA) for diagnosing and managing CAD. The platform seeks to automate anonymization, reporting, Agatston scoring, and plaque quantification, providing a comprehensive tool from diagnosis to clinical management and predicting responses to medication. The study involves two groups: 4000 patients who were clinically evaluated by CTCA between 2007 and 2017, and 4000 patients currently being evaluated by CTCA. CTCA is performed as part of routine care. The platform will analyze data to improve precision in stenosis reporting, Agatston scoring, plaque detection, and epicardial adipose tissue (EAT) analysis. Participants' CTCA scans and clinical data will be collected and analyzed using AI tools. Researchers will assess the AI platform's precision in reporting and scoring at baseline, with ongoing outcome analysis over one to five years. The study includes monitoring changes in coronary artery disease features and evaluating how the AI can aid clinical and industrial applications. Participation involves data collection from routine care without additional interventions, and the study will continue until 2027.
CONDITIONS
Brief Title
AI Driven National Platform for CT cOronary Angiography for clinicaL and industriaL applicatiOns Registry
Who Can Participate
Age: 21Years - 80Years
All Genders
Eligibility Criteria
You may qualify if you...
- Age 21 years or older
- Signed informed consent
- Clinically indicated for evaluation by CTCA
You will not qualify if you...
- Unable to provide informed consent
- Known complex congenital heart disease
- Planned invasive angiography for reasons other than CAD
- Non-cardiac illness with life expectancy less than 2 years
- Pregnancy
- Participation in another clinical trial involving investigational drug or device
- Prior cardiac event or coronary revascularization before CTCA
- Glomerular filtration rate 30 mL/min or less
- Known allergy to iodinated contrast agent
- Contraindications to beta blockers, nitroglycerin, or adenosine
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
1
2
3
Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
Diagnostic Evaluation
Duration - Single timepoint at baseline
Participants undergo Computed Tomography Coronary Angiography (CTCA) as part of routine care to evaluate coronary artery disease.
1 visit (in-person)
Long-term Monitoring
Duration - One to five years from baseline
Participants are monitored for clinical outcomes and AI analysis up to five years following the initial CTCA evaluation.
Trial Site Locations
Total: 3 locations
1
National Heart Centre Singapore
Singapore, Singapore, 169609
Actively Recruiting
2
National University Hospital
Singapore, Singapore
Actively Recruiting
3
Tan Tock Seng Hospital
Singapore, Singapore
Actively Recruiting
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Research Team
A
Associate Professor Zhong Liang
D
Dr Lohendran Baskaran
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
2
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Published Research Related To This Trial
Cohort profile: AI-driven national Platform for CCTA for clinicaL and industriaL applicatiOns (APOLLO).
Lohendran Baskaran, Shuang Leng, Utkarsh Dutta...
https://pubmed.ncbi.nlm.nih.gov/39622571Context-aware deep network for coronary artery stenosis classification in coronary CT angiography.
X Wang, S Leng, Z Lu...
https://pubmed.ncbi.nlm.nih.gov/38083399