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Screening Cardiometabolic Opportunities Using Transformative Echocardiography Artificial Intelligence (SCOUT Echo-AI)
Sponsor: Kaiser Permanente
Summary
The goal of this prospective, multicenter, open-label, blinded end-point pragmatic study is to evaluate an artificial intelligence (AI)-augmented echocardiography screening approach for early detection of metabolic dysfunction associated steatotic liver disease (MASLD) and/or cirrhosis, in patients undergoing routine transthoracic echocardiograms (TTEs). The main question it aims to answer is to: 1. Evaluate notification responsiveness and rates of confirmatory testing for patients identified as high risk for having liver disease to determine whether optimized notifications increase timely confirmatory testing and treatment initiation versus standard of care assessment. 2. Compare time to diagnosis, treatment uptake, and clinical outcomes (hospitalizations, incident ASCVD, mortality) between cohorts identified as high risk by the AI algorithm and comparison groups to determine whether AI guided screening shortens time to diagnosis and increases appropriate treatment.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
2000
Start Date
2026-01-01
Completion Date
2027-11-01
Last Updated
2025-11-17
Healthy Volunteers
No
Interventions
AI-Enabled Identification (EchoNet-Liver)
AI-generated notifications to clinicians about possible undiagnosed liver disease (MASLD and/or Cirrhosis) detected from Transthoracic Echocardiogram
Locations (4)
Cedars-Sinai Medical Center
Los Angeles, California, United States
Stanford Healthcare
Palo Alto, California, United States
Kaiser Permanente
Pleasanton, California, United States
Massachusetts General Hospital
Boston, Massachusetts, United States