Deep Learning CAD Screening on Chest CT

NCT ID: NCT07181512

Last Updated: 2025-09-18

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-09-16
• Study Completion Date: 2027-12-31

Brief Summary

Coronary artery disease (CAD) is one of the leading causes of death worldwide. Many people have early atherosclerosis without symptoms, and some may develop significant coronary stenosis before any warning signs appear. Identifying high-risk individuals at an early stage is important to prevent heart attacks and other cardiovascular events.

Coronary CT angiography (CCTA) can directly evaluate plaque type and the degree of narrowing in the coronary arteries, but it is expensive, requires contrast injection, and involves higher radiation, making it unsuitable for large-scale screening. In contrast, non-contrast chest CT is widely used for health check-ups and lung disease follow-up. Such scans often provide clear views of certain coronary segments, which creates an opportunity to screen for CAD without additional cost or risk.

This multicenter study aims to develop and validate deep learning models to analyze coronary calcified segments that are visible on non-contrast chest CT. Two main objectives are: (1) to predict whether calcified segments contain mixed plaque components (both calcified and non-calcified); and (2) to predict whether these segments have significant narrowing (≥50% stenosis) as determined by CCTA. The study will also describe how often ≥50% stenosis is found in non-calcified segments, in order to demonstrate their low-risk nature.

The study includes retrospective data collected between 2015 and 2024, and a prospective external validation cohort starting in 2025. Approximately 1,417 patients with paired chest CT and CCTA have already been included for model development and testing. An additional 200 or more patients will be prospectively recruited for external validation.

This research may provide evidence that deep learning applied to routine non-contrast chest CT can serve as an opportunistic tool for early CAD risk screening in the general population.

Conditions

Coronary Artery Disease; Coronary Artery Stenosis Stent

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: OTHER

Study Groups

Patients undergoing non-contrast chest CT and CCTA

A cohort of patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days. Clearly visualized coronary segments will be analyzed at the segment level for plaque composition and ≥50% stenosis using deep learning models. Both retrospective (2015-2024) and prospective (2025) cases are included.

Deep Learning Analysis of Non-contrast Chest CT

Intervention Type: OTHER

Analysis of clearly visualized coronary segments on non-contrast chest CT using deep learning models, compared with CCTA reference standard.

Interventions

Deep Learning Analysis of Non-contrast Chest CT

Analysis of clearly visualized coronary segments on non-contrast chest CT using deep learning models, compared with CCTA reference standard.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Age ≥18 years

2. Patients who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days

3. Coronary segments clearly visualized on non-contrast chest CT

Exclusion Criteria

1. Segments with motion artifacts, metal artifacts, or stents preventing analysis

2. Vessel lumen completely obscured by calcification (unrecognizable vascular course)

3. Inability to match coronary segment location between non-contrast chest CT and CCTA

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Jinhua Municipal Central Hospital

OTHER

Sponsor Role: collaborator

The Second Affiliated Hospital of Fujian Medical University

OTHER

Sponsor Role: collaborator

First Affiliated Hospital of Ningbo University

NETWORK

Sponsor Role: collaborator

Yifan Guo

OTHER_GOV

Sponsor Role: lead

Responsible Party

Yifan Guo

Lecturer, Department of Radiology, First Affiliated Hospital of Zhejiang Chinese Medical University

Responsibility Role: SPONSOR_INVESTIGATOR

Central Contacts

Yifan Guo, MD

Role: CONTACT

20193071@zcmu.edu.cn

+86-18072947783

Other Identifiers

CAD-AI-2025-V1.0

Identifier Type: -

Identifier Source: org_study_id