NCT06748261

AI-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary CTA

Artificial Intelligence-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary Computer Tomography Angiography

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 5,000 (estimated)
• Sponsor: Shanghai Zhongshan Hospital · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

Summary

The goal of this observational and diagnostic study is to develop and validate an artificial intelligence assisted approach for coronary computer tomography angiography-(CCTA)-based screening and diagnosis of cardiomyopathies in patients with suspected coronary artery diseases. This study aims to develop a computerized CCTA interpretation using artificial intelligence for multi-label classification task to assist cardiomyopathy diagnosis in the clinical workflow.

Detailed description

Cardiovascular diseases (CVD) are the leading causes of death and disability worldwide. With coronary artery disease accounting for a large proportion of CVD disease burden, coronary computer tomography angiography (CCTA) has become widely used for a comprehensive assessment of the total coronary atherosclerotic burden. In contrast, cardiac magnetic resonance (CMR) remains the gold standard for evaluating and diagnosing cardiomyopathies. However, clinical application of CMR has been hindered by the time and cost of examination and shortage of qualified doctors and staff. Consequently, the value of CCTA in screening and diagnosis in cardiomyopathies warrants further investigation. The ability of artificial intelligence to learn distinctive features and to recognize characteristic patterns on big data without extensive manual labor makes it highly effective for interpreting CCTA data. Although very few studies investigated the diagnostic value of CCTA for myocardiopathies, which is by far not established or applied in clinical practice by radiologists, automated image analysis has a clear advantage compared to humans by offering objective and uniform solutions. Further, whether a comprehensive, end-to-end, artificial intelligent approach can be used to analyse CCTA for diagnosis multi-classifications of cardiomyopathies remains unknown. Therefore, this study aims to develop and validate an artificial intelligence assisted approach on CCTA for screening and diagnosis of cardiomyopathies in patients with suspected coronary artery diseases.

Conditions

• Cardiovascular Diseases
• Hypertrophic Cardiomyopathy (HCM)
• Dilated Cardiomyopathy (DCM)
• Restrictive Cardiomyopathy
• Amyloid Cardiomyopathy
• Ischemic Cardiomyopathy
• Arrhythmogenic Right Ventricular Cardiomyopathy
• Myocarditis
• Cardiomyopathies

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	CCTAI model	Using a derivative sub-cohort, the investigators aim to first develop an CCTA-based AI-assisted (CCTAI) screening model to distinguish patients with cardiac abnormalities from those normal controls. Second, the investigators target at developing a CCTAI diagnostic model with multi-classification output of cardiomyopathy diagnosis. Both models will be tested in internal validation cohort and external validation cohort.

Timeline

• Start date: 2024-12-30
• Primary completion: 2025-06-30
• Completion: 2025-12-30
• First posted: 2024-12-27
• Last updated: 2024-12-27