Actively Recruiting

Age: 18Years +
All Genders
ID06410690

Trustworthy, Integrated Artificial Intelligence Tools for Predicting High-risk Coronary Plaques in Coronary Artery Disease Patients

Led by Centro Cardiologico Monzino · Updated on 2026-05-29

4000

Participants Needed

2

Research Sites

N/A

Total Duration

On this page

Sponsors

C

Centro Cardiologico Monzino

Lead Sponsor

F

Fondazione IRCCS Policlinico San Matteo di Pavia

Collaborating Sponsor

AI-Summary

What this Trial Is About

Researchers are studying coronary artery disease (CAD), a major cause of death and disability, to improve how patients at high risk of heart problems are identified. Current methods using imaging and known biomarkers do not work well enough. This research aims to develop a new, non-invasive AI-based tool that combines coronary artery imaging, plaque analysis, and genetic markers to better predict which patients with suspected or known CAD are at higher risk of heart events. The study involves three main phases. First, a retrospective group of 3,000 adults who had coronary CT angiography (CCTA) is used to develop AI tools for automatic artery and plaque analysis. This includes machine learning and computational methods validated against standard tests. The second phase applies these tools to a larger retrospective group, integrating genomic data to refine risk predictions. Finally, the AI model will be tested prospectively in a separate cohort from an ongoing study, with follow-up planned for 24 months after CCTA to check accuracy and reliability. Participants will undergo CCTA scans using advanced scanners and have data collected on coronary artery status, plaque characteristics, and genetic information. The study will assess how well the AI predicts vulnerable plaques and overall atherosclerotic burden linked to heart events. Researchers will monitor patient outcomes over time, using clinical data and follow-up visits to measure the AI tool's performance. The entire process aims to create a trustworthy, integrated AI system for better risk assessment in CAD patients.

CONDITIONS

Brief Title

Trustworthy, Integrated Artificial Intelligence Tools for Predicting High-risk CORonary PlaqueS

Who Can Participate

Age: 18Years +
All Genders

Eligibility Criteria

Eligible

You may qualify if you...

  • Patients aged 18 years or older with known or suspected coronary artery disease referred for diagnostic evaluation
  • Coronary CT angiography performed with state-of-the-art scanners having more than 64 slices
Not Eligible

You will not qualify if you...

  • Any non-invasive diagnostic test performed within 90 days before enrollment
  • Low-to-intermediate pre-test likelihood of coronary artery disease by updated Diamond-Forrester score
  • Acute coronary syndrome
  • Evidence of clinical instability
  • Contraindication to contrast agent or impaired kidney function
  • Inability to hold breath during imaging
  • Pregnancy
  • Cardiac arrhythmias
  • Presence of pacemaker or implantable defibrillator
  • Contraindications to sublingual nitrates, beta-blockers, or adenosine
  • Structural cardiomyopathy

AI-Screening

AI-Powered Screening

Complete this quick 3-step screening to check your eligibility

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Your Study Journey

Screening

Duration - 2 to 4 weeks

Participants are screened for eligibility to participate in the trial.

1 visit (in-person)

Diagnostic Evaluation

Duration - 1 day

Participants undergo coronary CT angiography (CCTA) and related diagnostic procedures to assess coronary artery disease and plaque characteristics.

1 visit (in-person)

Long-term Monitoring

Duration - 24 months

Participants are followed for up to 24 months to monitor coronary artery disease progression and assess high-risk plaque features.

Periodic follow-up visits depending on cohort assignment

Trial Site Locations

Total: 2 locations

1

Centro Cardiologico Monzino

Milan, Milan, Italy, 20131

Actively Recruiting

2

Centro Cardiologico Monzino

Milan, MI, Italy, 20131

Actively Recruiting

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Research Team

G

Gianluca Pontone

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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Frequently Asked Questions

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