Feasibility of a Deep Learning-based Algorithm for Non-invasive Assessment of Vulnerable Coronary Plaque (FOCUS DL)

General Electric (GE)

Status: Not yet enrolling

Conditions: Coronary Artery Disease; Acute Coronary Syndrome; Stable Angina; Chronic Coronary Syndrome

Treatments: Other: Deep Learning-based Vulnerable Plaque Detection and Assessment Tool

Study type: Observational

Funder types: Industry; Other

Identifiers

NCT06186336

12019187648

Details and patient eligibility

About

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.

Full description

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.

Enrollment: 200 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

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.

Trial design

200 participants in 1 patient group

DL-Based Vulnerable Plaque Detection and Assessment Tool

Description:

Enrolled subjects will receive a clinically indicated CCTA and ICA with OCT within 10 days. At least two qualified CCTA radiologists will independently review and annotate the coronary plaques in the CCTA using their local post-processing tools. At least two trained OCT readers will review and annotate the coronary plaques in the ICA/OCT using their local post-processing tools. The original de-identified CCTA data will be inputted into the Vulnerable Plaque Detection and Assessment Tool. The tool will perform the automatic identification of the plaque location and characteristics. These results will be compared to assess the algorithm's performance.

Treatment:

Other: Deep Learning-based Vulnerable Plaque Detection and Assessment Tool