AI-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary CTA (Atlantis)

Fudan University

Status: Not yet enrolling

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

Treatments: Diagnostic Test: CCTAI model

Study type: Observational

Funder types: Other

Identifiers

NCT06748261

ZS-CCTAI

Details and patient eligibility

About

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.

Full 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.

Enrollment: 5,000 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

Inclusion and exclusion criteria

Cardiomyopathy cohort:

• Inclusion Criteria:

  1. A clinical diagnosis of cardiomyopathies, including hypertrophic cardiomyopathy, dilated cardiomyopathy, restrictive cardiomyopathy, cardiac amyloidosis, myocarditis, arrhythmogenic right ventricular cardiomyopathy, and coronary artery disease/ischemic heart disease.
  2. At least one CCTA before surgery or implantable device treatment.

• Exclusion Criteria:

  1. No recorded diagnosis of cardiomyopathy or undetermined type of cardiomyopathy.
  2. A clinical diagnosis of secondary cardiac abnormalities due to other organic or systemic diseases.
  3. Surgery or implantable device treatment before CCTA examination.

Control cohort:

• Inclusion Criteria: participants with at least one CCTA examination.
• Exclusion Criteria: clinical diagnosis of cardiovascular diseases (including cardiomyopathy, history of myocardial infarction, history of cardiac surgery, stent implantation, ICD implantation and so on) or secondary cardiac abnormalities due to systemic diseases.

Trial design

5,000 participants in 2 patient groups

Cardiomyopathy cohort

Description:

Patients who have underwent CCTA examination and have recorded diagnosis of cardiomyopathy are enrolled in the cardiomyopathy cohort. Clinical diagnosis of cardiomyopathies based on patients' complete electrical medical record (EMR), encompassing clinical presentations, family history, laboratory results, ECG, echocardiogram, all available imaging assessments (if any, i.e. cardiac magnetic resonance, single-photon emission computed tomography, and invasive coronary angiography), and myocardial biopsy (if any). Clinical diagnoses are retrieved from (EMR) and used as ground truth for AI-assisted CCTA-based screening and diagnostic model developing.

Treatment:

Diagnostic Test: CCTAI model

Control cohort

Description:

Participants who have CCTA examination are recruited in the control cohort given that his or her medical record rules out cardiovascular diseases (including cardiomyopathy, history of myocardial infarction, history of cardiac surgery, stent implantation, ICD implantation and so on) and secondary cardiac abnormalities due to systemic diseases.

Treatment:

Diagnostic Test: CCTAI model