AI Assisted Screening for VHD Using Routine Chest CT Scans

NCT07449130 | Recruiting

• 1 other identifier: 2025-1250
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 3

Brief Summary

This is a prospective, multicenter study designed to validate a deep learning model for screening valvular heart diseases using routine, non-contrast chest computed tomography (CT) scans. The primary objective is to evaluate the model's diagnostic performance, with the sensitivity serving as the primary efficacy endpoint. Secondary endpoints will include other performance metrics such as area under the receiver operating characteristic curve (AUC), specificity, and accuracy, etc.

Conditions

Heart Valve Diseases

Outcome Measures

Primary Outcomes (1)

• Sensitivity

  Sensitivity: Measures the proportion of patients with moderate-to-severe disease that the model correctly identifies.

  1 year

Secondary Outcomes (3)

• Area Under the Receiver Operating Characteristic Curve (AUC)

  1 year

• Accuracy

  1 year

• Specificity

  1 year

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

People aged 18 and above in any medical context of hospitals.

You may qualify if:

• Age ≥ 18 years.
• Complete electronic health record.
• Non-contrast chest CT performed between Nov 1, 2025 - Nov 1, 2026 in any medical context (including physical exam, outpatient, inpatient, or emergency).
• AI-predicted moderate or severe valvular heart disease, or deemed to require clinical intervention, or selected negative cases from sampling verification.

You may not qualify if:

• Poor-quality non-contrast chest CT images.
• Incomplete clinical records, involving severe deficiencies in critical diagnostic results, treatment records, imaging data, surgical records, medical history summaries, laboratory test results, or other essential medical information.
• Presence of prosthetic valve implants, including aortic valves (mechanical valves, bioprosthetic valves), mitral valves (transcatheter edge-to-edge repair, bioprosthetic valves, mechanical valves, annuloplasty rings), tricuspid valves (TEER clipping, bioprosthetic valves, mechanical valves, annuloplasty rings), pulmonary valves (bioprosthetic valves), etc.

Sponsors & Collaborators

• Second Affiliated Hospital, School of Medicine, Zhejiang University: lead

Study Sites (3)

Renmin Hospital of Wuhan University

Wuhan, Hubei, China

RECRUITING

Xinjiang Uygur Autonomous Region People's Hospital

Ürümqi, Xinjiang, China

RECRUITING

The Second Affiliated Hospital of Zhejiang University School of Medicine

Hangzhou, Zhejiang, China

RECRUITING

MeSH Terms

Conditions

Heart Valve Diseases

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Central Study Contacts

Jian'an Wang, MD

CONTACT

+86-571-8778-4808; wangjianan111@zju.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 27, 2026
• First Posted: March 4, 2026
• Study Start: March 2, 2026
• Primary Completion (Estimated): November 1, 2026
• Study Completion (Estimated): November 1, 2026
• Last Updated: May 12, 2026

Record last verified: 2026-03

Locations

CN (3)