Atrial Fibrillation
Conditions
Brief 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.
Interventions
A deep learning model will identify the intracardiac thrombus. The AI model will produce an assessment of intracardiac thrombus using video based features.
Cardiac electrophysiologists use their own experience to determine whether there is intracardiac thrombus
Sponsors
Study design
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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Degree of change from initial (AI vs EP doctor) assessment to final cardiologist assessment | 10 Minutes |
Secondary
| Measure | Time frame |
|---|---|
| Perioperative adverse event rates | 10 Minutes |
Countries
China