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NCT Number: NCT07216859

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

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.

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Key information

Conditions

MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease Cirrhosis Fibrosis Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Cedars-Sinai Medical Center, Los Angeles, California, United States

Loading trial locations.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Adults ≥18 years.
  • Underwent routine TTE within site defined recent timeframe and flagged as high risk for MASLD and/or cirrhosis by the AI model using pre specified threshold.
  • Able to provide informed consent; reachable for follow up.

Exclusion criteria

  • Inability to consent or communicate.
  • Enrollment in hospice or life expectancy so limited that additional evaluation would not be appropriate per clinician judgment.
  • Clinical circumstances where immediate alternative diagnostic pathways supersede study procedures (e.g., acute decompensation requiring urgent management).
  • Prior liver or kidney transplant.
  • Patient unwilling to undergo prospective testing for liver disease.

Treatment and study plan

AI-Enabled Identification (EchoNet-Liver)

Other

AI-generated notifications to clinicians about possible undiagnosed liver disease (MASLD and/or Cirrhosis) detected from Transthoracic Echocardiogram

Primary outcomes

  1. 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.

    Numerator: Participants with clinician-confirmed diagnosis of later stage MASLD and/or cirrhosis after confirmatory evaluation.

    Denominator:

    • Participants with positive AI screen who were enrolled and evaluated.
    • The intervention is the clinician referral or referral testing workflow. The clinicians ultimately have discretion to avoid further downstream testing if pretest probability is felt to be too low. If a clinician determines no further testing is warranted despite high risk assessment by AI, the participant will be classified as a false positive (still counted in the denominator).

Secondary outcomes

  1. Time to diagnosis of MASLD/cirrhosis

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification to first confirmatory diagnosis

  2. Time to diagnosis for MASLD with F2 fibrosis or greater

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification to first confirmatory diagnosis

  3. Time to diagnosis for steatotic liver disease

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification to first confirmatory diagnosis

  4. Time to confirmatory imaging

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification

  5. Time to initiation of targeted treatment

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification

  6. All-cause mortality

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification

  7. All-cause hospitalization

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification

  8. Heart failure hospitalization

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification (Defined as admission with IV diuretics or elevated BNP)

  9. Cardiovascular hospitalization

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification for Cardiovascular hospitalization (defined by principal ICD9/10 code)

  10. Hepatic decompensation hospitalization

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification for hepatic decompensation hospitalization (defined by ascites, hepatic encephalopathy, variceal bleeding, hepatocellular carcinoma, or liver transplantation)

  11. New ASCVD diagnosis

    Time frame: Followed up to 24 months post notification.

    Time (days) from AI identification

Sponsors and collaborators

Lead sponsor

Kaiser Permanente

Other

Collaborators

  • Cedars-Sinai Medical Center
  • Massachusetts General Hospital
  • Stanford University

Registry information

Acronym: SCOUT Echo-AI

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Oct 15, 2025
Registry last updated
Nov 17, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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