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Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT06206187
Other study ID # ICE Detector-RCT
Secondary ID
Status Not yet recruiting
Phase N/A
First received
Last updated
Start date January 5, 2024
Est. completion date December 31, 2025

Study information

Verified date January 2024
Source Shanghai Chest Hospital
Contact Shaohui Wu, PHD
Phone 15821960839
Email wushaohui18@163.com
Is FDA regulated No
Health authority
Study type Interventional

Clinical 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 echocardiograms.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 1500
Est. completion date December 31, 2025
Est. primary completion date July 31, 2025
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years to 90 Years
Eligibility Inclusion Criteria: 1. Aged 18-80 years. 2. Willing to sign informed consent. 3. Patients diagnosed with atrial fibrillation Paroxysmal AF and Persistent AF according to the latest clinical guidelines Exclusion Criteria: 1. End-stage disease with a mean life expectancy less than 1 year 2. New York Heart Association (NYHA) class III or IV, or last known left ventricular ejection fraction less than 30% 3. Previous surgical or catheter ablation for AF 4. Bradycardia and presence of implanted ICD 5. Uncontrolled hypertension: Systolic blood pressure (SBP) >180 mmHg or diastolic blood pressure (DBP) > 110 mmHg 6. Patients with Cardiovascular events including acute myocardial infarction, any PCI, valvular cardiac surgical, or percutaneous procedure within the past 3 months 7. Women of childbearing potential who are, or plan to become, pregnant during the time of the study 8. Have been enrolled in an investigational study evaluating devices or drugs.

Study Design


Related Conditions & MeSH terms


Intervention

Other:
Automated detection of the intracardiac thrombus through deep learning
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 thrombus
Cardiac electrophysiologists use their own experience to determine whether there is intracardiac thrombus

Locations

Country Name City State
China Shanghai Chest Hospital Shanghai ???

Sponsors (2)

Lead Sponsor Collaborator
Shanghai Chest Hospital Johnson & Johnson

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Degree of change from initial (AI vs EP doctor) assessment to final cardiologist assessment 10 Minutes
Secondary Perioperative adverse event rates 10 Minutes
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