Screening Cardiometabolic Opportunities Using Transformative Echocardiography Artificial Intelligence (SCOUT Echo-AI)
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorKaiser Permanente
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial design
Design model
Single group
Treatments tested in this trial
AI-Enabled Identification (EchoNet-Liver)
Other interventionAI-generated notifications to clinicians about possible undiagnosed liver disease (MASLD and/or Cirrhosis) detected from Transthoracic Echocardiogram
Treatment groups
2,000 Participants
are divided into 1 treatment groupGroup A: AI Notification (EchoNet-Liver-Flagged patients)Experimental treatment 1 intervention
Trial outcomes
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).
Time frame
From enrollment to end of follow up at 1 year.Secondary outcomes
1
Time to diagnosis of MASLD/cirrhosis
Time (days) from AI identification to first confirmatory diagnosis
Time frame
Followed up to 24 months post notification.2
Time to diagnosis for MASLD with F2 fibrosis or greater
Time (days) from AI identification to first confirmatory diagnosis
Time frame
Followed up to 24 months post notification.3
Time to diagnosis for steatotic liver disease
Time (days) from AI identification to first confirmatory diagnosis
Time frame
Followed up to 24 months post notification.4
Time to confirmatory imaging
Time (days) from AI identification
Time frame
Followed up to 24 months post notification.Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Kaiser Permanente
Lead sponsor
Stanford University
Collaborator
Massachusetts General Hospital
Collaborator
Cedars-Sinai Medical Center
Collaborator
Source ClinicalTrials.gov
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