Screening Cardiometabolic Opportunities Using Transformative Echocardiography Artificial Intelligence (SCOUT Echo-AI)

Part of paid clinical trials in Los Angeles, California.

Sponsor
Kaiser Permanente
Study ID
NCT07216859
Status
Not Yet Recruiting

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Conditions

  • Cirrhosis
  • MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • AI-Enabled Identification (EchoNet-Liver) — OTHER
    AI-generated notifications to clinicians about possible undiagnosed liver disease (MASLD and/or Cirrhosis) detected from Transthoracic Echocardiogram

Study Details

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 Dates

Start date
Jan 1, 2026
Status verified
Nov 2025
Primary completion
Nov 1, 2027
Completion
Nov 1, 2027

Study Design

Enrollment
2,000 participants (estimated)
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC

Arms

  • Experimental: AI Notification (EchoNet-Liver-Flagged patients)
    Participants whose prior transthoracic echocardiograms are flagged by an AI model (EchoNet-Liver) as high risk for MASLD and/or cirrhosis, a notification is delivered to the primary treating clinician, or undergoes a structured diagnostic workflow.

Primary Outcome Measure

Positive Predictive Value (PPV) of the AI algorithm for detecting MASLD and/or cirrhosis confirmed within 12 months of AI identification. [ Time Frame: From enrollment to end of follow up at 1 year. ]

Locations (4)

FacilityCityStateZIPSite coordinators
Cedars-Sinai Medical CenterLos AngelesCalifornia90034
Alan Kwan
[email protected]
3104233475
Alan Kwan (PRINCIPAL_INVESTIGATOR)
Stanford HealthcarePalo AltoCalifornia94588
Alex Sandhu
[email protected]
6507236459
Alex Sandhu (PRINCIPAL_INVESTIGATOR)
Kaiser PermanentePleasantonCalifornia94588
Zara Fatima - Project Manager, Bachelor of Medicine & Surgery
[email protected]
9163859163
[email protected]
David Ouyang (PRINCIPAL_INVESTIGATOR)
Massachusetts General HospitalBostonMassachusetts02114
Long Nguyen
[email protected]
617-726-2426
Long Nguyen (PRINCIPAL_INVESTIGATOR)

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By research site
Cedars-Sinai Medical Center· Los Angeles, CAStanford Healthcare· Palo Alto, CAKaiser Permanente· Pleasanton, CAMassachusetts General Hospital· Boston, MA

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