An observational study in Cardiovascular Diseases, Hypertrophic Cardiomyopathy (HCM) and Dilated Cardiomyopathy (DCM), sponsored by Shanghai Zhongshan Hospital. Not yet recruiting. Per ClinicalTrials.gov, last updated 2024-12-27.
Sponsored by Shanghai Zhongshan Hospital · Observational
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.
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.
553 studies on the registry are indexed under Cardiomyopathies; 152 are open to participants now.
This study's planned enrollment of 5,000 is above the median of 200 across 263 observational studies indexed under Cardiomyopathies.
Browse Cardiomyopathies studies →Shanghai Zhongshan Hospital is the lead sponsor of 652 studies on the registry; 291 are open to participants now.
Compared within Cardiomyopathies, this study's primary condition as ClinicalTrials.gg classifies it; the registry's first condition term is Cardiovascular Diseases. The counts are of registry records indexed under each term, refreshed daily. How we classify →
Consecutive candidates have at least one CCTA between 1/1/2014 and 31/12/2024 will be collected.
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Cardiomyopathy cohort:
Inclusion Criteria:
No recorded diagnosis of cardiomyopathy or undetermined type of cardiomyopathy.
Control 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.
Diagnostic Test: CCTAI model
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.
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.
Diagnostic performance
The performance of the AI models is evaluated by assessing their sensitivity, specificity, precision and F1 score (harmonic mean of the predictive positive value and sensitivity), with two-sided 95% CIs, as well as the AUC of the ROC with two-sided CIs. The F1 score is complementary to the AUC, which is particularly useful in the setting of multiclass prediction and less sensitive than the AUC in settings of class imbalance. For an aggregate measure of model performance, the investigators compute the class frequency-weighted mean for the F1 score and the AUC. Other diagnostic performance assessing metrics include true-positive rate, true-negative rate, false-positive rate, false-negative rate, precision, sensitivity (recall), specificity, positive predictive value, and negative predictive value.
Time frame: CCTA examination before surgical or interventional treatments.
No study locations are listed for this record.
Plan to share: No
No publications or documents are linked to this record.
Start is estimated for Dec 2024; primary completion for Jun 2025, 6 months later.
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This study is not yet recruiting, as verified in Dec 2024. You cannot join it, but the record below documents what was studied.
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