AI-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary CTA
NCT ID: NCT06748261
Last Updated: 2024-12-27
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
5000 participants
OBSERVATIONAL
2024-12-30
2025-12-30
Brief Summary
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Detailed Description
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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
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Keywords
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Study Design
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CASE_CONTROL
CROSS_SECTIONAL
Study Groups
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Cardiomyopathy cohort
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.
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.
Control cohort
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.
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
2. At least one CCTA before surgery or implantable device treatment.
Exclusion Criteria
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:
ALL
Yes
Sponsors
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Shanghai Zhongshan Hospital
OTHER
Responsible Party
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Junbo Ge
Prof
Principal Investigators
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Chenguang Li, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Fudan University
Central Contacts
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Other Identifiers
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ZS-CCTAI
Identifier Type: -
Identifier Source: org_study_id