AI Assisted Screening for VHD Using Routine Chest CT Scans
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Heart Valve Diseases. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Artificial-Intelligence Assisted Opportunistic Screening for Valvular Heart Disease Using Non-contrast Chest CT Scans: A Prospective, Multicenter Study
Overview
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.
Detailed description
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 from individuals in physical examination and outpatient clinics within a hospital alliance.
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.
Participants from the target populations will undergo a routine non-contrast chest CT scan. The deep learning model will analyze these images in real-time. For those identified by the model as having moderate-to-severe heart valve disease, a confirmatory echocardiogram will be performed immediately. The echocardiogram results will serve as the reference standard for diagnosis. Statistical analyses will be performed to assess the model's performance against this reference, including calculating the 95% confidence interval for the AUC.
As this study only involves standard, low-radiation diagnostic imaging procedures (non-contrast CT and echocardiography) that are part of routine clinical care, it is considered to pose no additional relevant safety risks to participants. The total study duration is estimated to be 12 months.
Primary outcome measures
- Sensitivity [Time frame: 1 year]
Secondary outcome measures (3)
- Area Under the Receiver Operating Characteristic Curve (AUC) [Time frame: 1 year]
- Accuracy [Time frame: 1 year]
- Specificity [Time frame: 1 year]
Eligibility criteria
Inclusion criteria
- 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.
Exclusion criteria
- 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.
- Abnormalities or conditions deemed by the investigator to warrant exclusion from the study enrollment.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
China · 3 centers
- Renmin Hospital of Wuhan University — Wuhan
- Xinjiang Uygur Autonomous Region People's Hospital — Ürümqi
- The Second Affiliated Hospital of Zhejiang University School of Medicine — Hangzhou
Identifiers
NCT: NCT07449130 · 2025-1250