Blinded Randomized Controlled Trial of Artificial Intelligence Guided Detection of Intracardiac Thrombus

Shanghai Jiao Tong University

Status: Not yet enrolling

Conditions: Atrial Fibrillation

Treatments: Other: Automated detection of the intracardiac thrombus through deep learning; Other: Electrophysiologist judgment of the intracardiac thrombus

Study type: Interventional

Funder types: Other; Industry

Identifiers

NCT06206187

ICE Detector-RCT

Details and patient eligibility

About

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.

Enrollment: 1,500 estimated patients

Sex: All

Ages: 18 to 90 years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

---

1,500 participants in 2 patient groups

Electrophysiologist judgment

Active Comparator group

Treatment:

Other: Electrophysiologist judgment of the intracardiac thrombus

Artificial Intelligence Detection

Experimental group

Treatment:

Other: Automated detection of the intracardiac thrombus through deep learning