Atrial FibrillationNCT06206187
Blinded Randomized Controlled Trial of Artificial Intelligence Guided Detection of Intracardiac Thrombus
A study testing Automated detection of the intracardiac thrombus through deep learning and Electrophysiologist judgment of the intracardiac thrombus in people with Atrial Fibrillation, run by Shanghai Chest Hospital at 1 site in China.
Trial summary
To determine whether an integrated AI decision support can save time and improve the accuracy of detection of intracardiac thrombus, the investigators are conducting a blinded, randomized controlled study of AI-guided detection of intracardiac thrombus to electrophysiologist judgment in preliminary readings of… Read more about this study
- Who is it for?
- Ages 18 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Atrial Fibrillation
- What is being tested?
- Automated detection of the intracardiac thrombus through deep learning and Electrophysiologist judgment of the intracardiac thrombus
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,500 participants
- Where
- 1 siteChina
- Study dates
- 5 Jan 2024to 31 Dec 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 18 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Aged 18-80 years.
- Willing to sign informed consent.
- Patients diagnosed with atrial fibrillation Paroxysmal AF and Persistent AF according to the latest clinical guidelines
Who cannot join
- End-stage disease with a mean life expectancy less than 1 year
- New York Heart Association (NYHA) class III or IV, or last known left ventricular ejection fraction less than 30%
- Previous surgical or catheter ablation for AF
- Bradycardia and presence of implanted ICD
- Uncontrolled hypertension: Systolic blood pressure (SBP) >180 mmHg or diastolic blood pressure (DBP) > 110 mmHg
- Patients with Cardiovascular events including acute myocardial infarction, any PCI, valvular cardiac surgical, or percutaneous procedure within the past 3 months
- Women of childbearing potential who are, or plan to become, pregnant during the time of the study
- Have been enrolled in an investigational study evaluating devices or drugs.
Trial locations and contacts
1 site in 1 country.
China1 site
Shanghai Chest Hospital
Shanghai, Shanghai Municipality, 200030
绍辉 吴, 15821960839, wushaohui18@163.com
About this study
Official title: ICE Detector-RCT: Blinded, Randomized Controlled Trial of Electrophysiologist vs. Artificial Intelligence Detection of Intracardiac Thrombus
To determine whether an integrated AI decision support can save time and improve the accuracy of detection of intracardiac thrombus, the investigators are conducting a blinded, randomized controlled study of AI-guided detection of intracardiac thrombus to electrophysiologist judgment in preliminary readings of echocardiograms.
- Conditions
- Atrial Fibrillation
- Being tested
- Automated detection of the intracardiac thrombus through deep learning, Electrophysiologist judgment of the intracardiac thrombus
- Medical subject headings
- Atrial Fibrillation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Automated detection of the intracardiac thrombus through deep learningOther
A deep learning model will identify the intracardiac thrombus. The AI model will produce an assessment of intracardiac thrombus using video based features.
Electrophysiologist judgment of the intracardiac thrombusOther
Cardiac electrophysiologists use their own experience to determine whether there is intracardiac thrombus
| Study group | Role | What they receive |
|---|---|---|
| Electrophysiologist judgment | Active comparator | Not described Receives: Other: Electrophysiologist judgment of the intracardiac thrombus |
| Artificial Intelligence Detection | Experimental | Not described Receives: Other: Automated detection of the intracardiac thrombus through deep learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
Degree of change from initial (AI vs EP doctor) assessment to final cardiologist assessment
Measured: 10 Minutes
Secondary outcomes (1)
Perioperative adverse event rates
Measured: 10 Minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jan 2024
- Study start5 Jan 2024 (estimated)
- Primary completion31 Jul 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Atrial FibrillationStudies of the same condition, open and under way.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai Chest Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Johnson & Johnson
- Responsible party
- Principal investigator, Xu Liu, Shanghai Chest Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06206187
- Sponsor's protocol ID
- ICE Detector-RCT
- Status last verified
- Jan 2024
- First submitted
- 4 Jan 2024
- Last updated
- 16 Jan 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 16 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ICE Detector-RCT: Blinded, Randomized Controlled Trial of Electrophysiologist vs. Artificial Intelligence Detection of Intracardiac Thrombus
- ClinicalTrials.gov ID
- NCT06206187
- Acronym / sponsor ID
- ICE Detector-RCT
- Conditions
- Atrial Fibrillation
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Shanghai Chest HospitalUniversity, hospital or other
- Collaborators
- Johnson & Johnson
- Study start
- 5 Jan 2024 (estimated)
- Primary completion
- 31 Jul 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 16 Jan 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.
To determine whether an integrated AI decision support can save time and improve the accuracy of detection of intracardiac thrombus, the investigators are conducting a blinded, randomized controlled study of AI-guided detection of intracardiac thrombus to electrophysiologist judgment in preliminary readings of echocardiograms.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Electrophysiologist judgment | Active comparator | Other: Electrophysiologist judgment of the intracardiac thrombus |
| Artificial Intelligence Detection | Experimental | Other: Automated detection of the intracardiac thrombus through deep learning |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Aged 18-80 years.
- Willing to sign informed consent.
- Patients diagnosed with atrial fibrillation Paroxysmal AF and Persistent AF according to the latest clinical guidelines
Key exclusion criteria (8 listed)
- End-stage disease with a mean life expectancy less than 1 year
- New York Heart Association (NYHA) class III or IV, or last known left ventricular ejection fraction less than 30%
- Previous surgical or catheter ablation for AF
- Bradycardia and presence of implanted ICD
- Uncontrolled hypertension: Systolic blood pressure (SBP) >180 mmHg or diastolic blood pressure (DBP) > 110 mmHg
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 |
|---|---|---|
| Automated detection of the intracardiac thrombus through deep learningA deep learning model will identify the intracardiac thrombus. The AI model will produce an assessment of intracardiac thrombus using video based features. | Other | Artificial Intelligence Detection |
| Electrophysiologist judgment of the intracardiac thrombusCardiac electrophysiologists use their own experience to determine whether there is intracardiac thrombus | Other | Electrophysiologist judgment |
Primary outcome
Degree of change from initial (AI vs EP doctor) assessment to final cardiologist assessment
Time frame: 10 Minutes
1 secondary outcome
Perioperative adverse event rates
Time frame: 10 Minutes
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Jan 2024.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Automated detection of the intracardiac thrombus through deep learning and Electrophysiologist judgment of the intracardiac thrombus in people with Atrial Fibrillation, run by Shanghai Chest Hospital at 1 site in China.
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 3 inclusion and 8 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: status not confirmed, as of the sponsor's last update on 16 Jan 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Shaohui Wu, PHD (15821960839, wushaohui18@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Degree of change from initial (AI vs EP doctor) assessment to final cardiologist assessment (time frame: 10 Minutes). 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 16 Jan 2024.