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

NCT07216859 | Not Yet Recruiting

• 2 other identifiers: 235633425AHAAI1487693
• Study Type: interventional
• Target: 2,000
• Locations: 1 country
• Sites: 4

Brief 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. 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. 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.

Conditions

MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease; Cirrhosis

Outcome Measures

Primary Outcomes (1)

• Positive Predictive Value (PPV) of the AI algorithm for detecting MASLD and/or cirrhosis confirmed within 12 months of AI identification.

  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).

  From enrollment to end of follow up at 1 year.

Secondary Outcomes (11)

• Time to diagnosis of MASLD/cirrhosis

  Followed up to 24 months post notification.

• Time to diagnosis for MASLD with F2 fibrosis or greater

  Followed up to 24 months post notification.

• Time to diagnosis for steatotic liver disease

  Followed up to 24 months post notification.

• Time to confirmatory imaging

  Followed up to 24 months post notification.

• Time to initiation of targeted treatment

  Followed up to 24 months post notification.

Study Arms (1)

AI Notification (EchoNet-Liver-Flagged patients)

EXPERIMENTAL

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.

Other: AI-Enabled Identification (EchoNet-Liver)

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

AI Notification (EchoNet-Liver-Flagged patients)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Kaiser Permanente: lead
• Stanford University: collaborator
• Massachusetts General Hospital: collaborator
• Cedars-Sinai Medical Center: collaborator

Study Sites (4)

Cedars-Sinai Medical Center

Los Angeles, California, 90034, United States

Location

Stanford Healthcare

Palo Alto, California, 94588, United States

Location

Kaiser Permanente

Pleasanton, California, 94588, United States

Location

Massachusetts General Hospital

Boston, Massachusetts, 02114, United States

Location

MeSH Terms

Conditions

Fibrosis

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Int Med-Cardio Non-Invasive

Study Record Dates

• First Submitted: October 13, 2025
• First Posted: October 15, 2025
• Study Start: January 1, 2026
• Primary Completion (Estimated): November 1, 2027
• Study Completion (Estimated): November 1, 2027
• Last Updated: November 17, 2025

Record last verified: 2025-11

Locations

US (4)