Heart Valve Diseases
Conditions
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.
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 any medical context 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.
Interventions
None listed
Sponsors
Second Affiliated Hospital, Zhejiang University, School of Medicine
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Age ≥ 18 years. 2. Complete electronic health record. 3. Non-contrast chest CT performed between Nov 1, 2025 - Nov 1, 2026 in any medical context (including physical exam, outpatient, inpatient, or emergency). 4. AI-predicted moderate or severe valvular heart disease, or deemed to require clinical intervention, or selected negative cases from sampling verification.
Exclusion criteria
1. Poor-quality non-contrast chest CT images. 2. 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. 3. 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. 4. Abnormalities or conditions deemed by the investigator to warrant exclusion from the study enrollment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | 1 year | Sensitivity: Measures the proportion of patients with moderate-to-severe disease that the model correctly identifies. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve (AUC) | 1 year | AUC for a model distinguishing valvular disease severity. The AUC value for this model would indicate how well it can correctly identify patients with moderate-to-severe disease from those with normal-to-mild disease. |
| Accuracy | 1 year | Accuracy: Measures the overall proportion of all patients that the model correctly classifies into either group. |
| Specificity | 1 year | Specificity: Measures the proportion of patients with normal-to-mild disease that the model correctly identifies. |
Countries
China
Contacts
CONTACTJian'an Wang, MD
Outcome results
None listed