Cardiovascular DiseasesAtlantisNCT06748261
AI-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary CTA
An observational study of Cardiovascular Diseases and Hypertrophic Cardiomyopathy (HCM), run by Shanghai Zhongshan Hospital.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Cardiovascular Diseases, Hypertrophic Cardiomyopathy (HCM), Dilated Cardiomyopathy (DCM)
- What is being tested?
- CCTAI model
- How many people
- 5,000 participants
- Where
- No sites listed yet
- Study dates
- 30 Dec 2024to 30 Dec 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Consecutive candidates have at least one CCTA between 1/1/2014 and 31/12/2024 will be collected.
Who may be able to join
- 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.
- At least one CCTA before surgery or implantable device treatment.
Who cannot join
- No recorded diagnosis of cardiomyopathy or undetermined type of cardiomyopathy.
- A clinical diagnosis of secondary cardiac abnormalities due to other organic or systemic diseases.
- 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 locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
Chenguang Li, MD, PhD contact
About this study
Official title: Artificial Intelligence-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary Computer Tomography Angiography
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.
Read the 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
- Being tested
- CCTAI model
- Keywords
- Cardiac computer tomography angiography, Cardiomyopathies, Artificial intelligence, Diagnosis
- Medical subject headings
- Cardiovascular Diseases, Cardiomyopathy, Hypertrophic, Cardiomyopathy, Dilated, Cardiomyopathy, Restrictive, Amyloid Neuropathies, Familial, Arrhythmogenic Right Ventricular Dysplasia, Myocarditis, Cardiomyopathies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CCTAI modelDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: CCTAI model | |
| 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. Receives: Diagnostic Test: CCTAI model |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: CCTA examination before surgical or interventional treatments.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Dec 2024
- Study start30 Dec 2024 (estimated)
- Primary completion30 Jun 2025 (estimated)
- Study completion30 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiovascular DiseasesStudies of the same condition, open and under way.
- More trials run by Shanghai Zhongshan HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Zhongshan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Junbo Ge, Shanghai Zhongshan Hospital
- Investigators
- Chenguang Li, MD, PhD, Principal investigator, Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06748261
- Sponsor's protocol ID
- ZS-CCTAI
- Acronym
- Atlantis
- Status last verified
- Dec 2024
- First submitted
- 17 Dec 2024
- Last updated
- 27 Dec 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-enabled Screening and Diagnosis of Cardiomyopathies Using Coronary Computer Tomography Angiography
- ClinicalTrials.gov ID
- NCT06748261
- Acronym / sponsor ID
- Atlantis / ZS-CCTAI
- Conditions
- Cardiovascular Diseases, Hypertrophic Cardiomyopathy (HCM), Dilated Cardiomyopathy (DCM), Restrictive Cardiomyopathy, Amyloid Cardiomyopathy, Ischemic Cardiomyopathy
- Keywords
- Cardiac computer tomography angiography, Cardiomyopathies, Artificial intelligence, Diagnosis
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Shanghai Zhongshan HospitalUniversity, hospital or other
- Study start
- 30 Dec 2024 (estimated)
- Primary completion
- 30 Jun 2025 (estimated)
- Study completion
- 30 Dec 2025 (estimated)
- First posted
- 27 Dec 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Cardiomyopathy cohort | – | Diagnostic Test: CCTAI modelPatients 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. |
| Control cohort | – | Diagnostic Test: CCTAI modelParticipants 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Consecutive candidates have at least one CCTA between 1/1/2014 and 31/12/2024 will be collected.
Key inclusion criteria (2 listed)
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CCTAI modelUsing 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 test | Cardiomyopathy cohort, Control cohort |
Primary outcome
Diagnostic performance
Time frame: CCTA examination before surgical or interventional treatments.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Dec 2024.
- Individual participant dataSharing planned: No
Related trials for Cardiovascular Diseases
Active, not recruitingPhase 3
ENDOTHELION Study Group: Effect of Bosentan in NAION Patients
Ischemic Optic Neuropathy
Active, not recruiting
Digital Reinforcement of Motivational Interviewing Micro-Skills Among Primary Care Healthcare Professionals
Hypertension (Without Type 2 Diabetes Mellitus)
Not yet recruitingPhase 2
Study of YKST02 in Adults With Relapsed or Refractory Multiple Myeloma
Relapsed or Refractory Multiple Myeloma (RRMM)
RecruitingPhase 2
Engaging T-cells to Eliminate MRD in Newly Diagnosed Myeloma Optimizing Response With Talquetmab and Teclistamab (ROTATE)
Multiple Myeloma
Recruiting
PAW-Hydrogel for Chronic Wound Healing
Diabetic Foot Ulcer (DFU), Pressure Ulcer (PU)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Chenguang Li, MD, PhD contact
Chenguang Li, MD, PhD principal investigator
Fudan University
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiovascular Diseases and Hypertrophic Cardiomyopathy (HCM), run by Shanghai Zhongshan Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 27 Dec 2024. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Junbo Ge, MD, PhD (008664041990, jbge@zs-hospital.sh.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance (time frame: CCTA examination before surgical or interventional treatments.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Dec 2024.