Deep-learning screening for coronary artery disease on routine chest CT

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This multicenter, retrospective analysis uses de-identified imaging from adults who underwent both non-contrast chest CT and coronary CT angiography (CCTA) within 30 days. Deep-learning models will be developed and validated to analyze calcified coronary segments visible on non-contrast chest CT, using CCTA as the reference standard. No additional imaging, radiation, or interventions are performed and the institutional review board approved a waiver of written consent. Segments with motion or metal artifacts, stents, or completely obscured lumens will be excluded and models will be tested across participating hospitals to assess generalizability.

Why it matters

Who can join: the full rules

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

Where it's running

• The First Affiliated Hospital of Zhejiang Chinese Medical University
  Hangzhou, Zhejiang, China Recruiting
• The First Affiliated Hospital of Ningbo University
  Ningbo, Zhejiang, China Recruiting

Study contacts

• Study coordinator: Yifan Guo, MD
• Email: 20193071@zcmu.edu.cn
• Phone: +86-18072947783

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.