Feasibility of a Deep Learning-based Algorithm for Non-invasive Assessment of Vulnerable Coronary Plaque

Feasibility of a Deep Learning-based Algorithm for Non-invasive Assessment of Vulnerable Coronary Plaque

The primary objective of this study is to assess the accuracy in terms of sensitivity, specificity, negative and positive predicted values of the DL-based algorithm with respect to correct identification of the plaque and associated vulnerability grade.

Data collected in this study will be used for technology development, scientific evaluation, education, and regulatory submissions for future products. This is a pre-market, open label, prospective, non-randomized clinical research study conducted at one site in Italy. The product being researched is the Deep Learning-based (DL) algorithm for non-invasive detection of vulnerable coronary plaque.

Inclusion Criteria:

1. Patients referred for a clinically indicated CCTA and ICA with OCT imaging examinations;
2. Diagnosis of chronic coronary syndrome, known CAD, or stable angina; AND,
3. Patients with ACS that may undergo a CCTA and not refer directly to the Cath lab for revascularization procedures.

Exclusion Criteria:

1. Contradictions to contrast;
2. Contraindications for beta blocker;
3. BMI >30;
4. High heart rate ≥75 BPM;
5. Atrial Fibrillation;
6. Arrythmia or irregular heartbeats;
7. Any prior coronary revascularization;
8. Presence of pacemaker or implantable cardioverter defibrillator; OR,
9. Patients with TAVI/TAVR.