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.
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.
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.
People aged 18 and above in any medical context of hospitals.
Cohort
Prospective
Not listed
Sensitivity
Sensitivity: Measures the proportion of patients with moderate-to-severe disease that the model correctly identifies.
Area Under the Receiver Operating Characteristic Curve (AUC)
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
Accuracy: Measures the overall proportion of all patients that the model correctly classifies into either group.
Specificity
Specificity: Measures the proportion of patients with normal-to-mild disease that the model correctly identifies.
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