The goal of this observational study is to learn if AI-assisted cardiac CT imaging can improve cardiovascular risk stratification and prediction of future coronary events in an adult population undergoing clinically indicated cardiac CT.
The main questions it aims to answer are:
* Can AI-enhanced cardiac CT accurately assess cardiovascular risk in a real-world adult population? * How do CT-derived plaque characteristics correlate with clinical, biochemical, and lifestyle risk factors? Researchers will compare subgroups (e.g., patients with different risk profiles, biomarkers, or imaging findings, and a subset undergoing OCT imaging) to see if differences in imaging and clinical parameters are associated with cardiovascular risk and plaque vulnerability.
Participants will:
* Provide informed consent and medical history/demographic information * Undergo blood sampling for cardiovascular and metabolic biomarkers * Have a resting ECG performed * Complete a detailed lifestyle and health questionnaire * Receive a non-invasive cardiac CT scan interpreted by an expert * Potentially receive heart rate-lowering medication (e.g., metoprolol) if required for imaging quality * Be referred for further clinical evaluation if clinically indicated 
Adults aged ≥18 years
Undergoing clinically indicated cardiac CT
Able and willing to provide informed consent
History of malignancy
Abnormal renal function (e.g., eGFR or serum creatinine outside reference range)
Contraindications to iodinated contrast agents or CT imaging
All-comers adult population undergoing clinically indicated cardiac CT in routine clinical care
Cohort
Prospective
Not listed
Cardiovascular risk stratification using AI-assisted cardiac CT
Assessment of cardiovascular risk based on AI-enhanced cardiac CT imaging, including coronary artery calcium scoring, plaque characterization, and integrated risk prediction using clinical and imaging data.
Correlation between CT-derived plaque characteristics and haematological biomarkers
Association between imaging-derived plaque features and blood biomarkers (e.g., CRP, lipid profile, ApoB/ApoA-1 ratio).
Concordance between coronary CT angiography (CCTA) and optical coherence tomography (OCT)
Agreement between CT-derived plaque characteristics and OCT findings in a subset of participants.
Association between lifestyle factors and imaging-derived plaque risk features
ssociation between lifestyle factors (e.g., smoking, diet, occupation) and imaging-derived Parameters.
Subgroup analyses of cardiovascular risk by demographic and clinical factors
Differences in imaging and risk profiles across subgroups (age, sex, comorbidities, hormonal history).
Development of an anonymized multimodal dataset for AI training and validation
Creation of a secure dataset integrating imaging, clinical, biomarker, and lifestyle data for future AI development.
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University of Galway
Lead sponsor
Science Foundation Ireland
Collaborator
