MASLD - Metabolic Dysfunction-Associated Steatotic Liver DiseaseSCOUT Echo-AINCT07216859
Screening Cardiometabolic Opportunities Using Transformative Echocardiography Artificial Intelligence (SCOUT Echo-AI)
A study testing AI-Enabled Identification (EchoNet-Liver) in people with MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease and Cirrhosis, run by Kaiser Permanente at 6 sites in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease, Cirrhosis
- What is being tested?
- AI-Enabled Identification (EchoNet-Liver)
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,000 participants
- Where
- 6 sitesUnited States
- Study dates
- 1 Jan 2028to 1 Nov 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
6 sites in 1 country. Contact the study team or a site near you to ask about taking part.
6 sites in 1 country
United States6 sites
Massachusetts General Hospital
Boston, Massachusetts, 02114
Long Nguyen, 617-726-2426, lnguyen24@mgh.harvard.edu
Long Nguyen
Northwestern Medicine
Chicago, Illinois, 60611
Faraz Ahmad - Site PI, MD, 413-687-7815, faraz.ahmad@northwestern.edu
Faraz Ahmad, MD
Cedars-Sinai Medical Center
Los Angeles, California, 90034
Alan Kwan, 3104233475, Alan.Kwan@cshs.org
Alan Kwan
UCLA
Los Angeles, California, 90095
Alex Sandhu, MD, 909-767-1214, ats114@stanford.edu
Alex Sandhu, MD
Stanford Healthcare
Palo Alto, California, 94588
Paul Cheng - Site PI, 6507236459, chengpa@stanford.edu
Paul Cheng
Kaiser Permanente
Pleasanton, California, 94588
Andrew Ambrosy - Site PI, MD, 9163859163, Andrew.P.Ambrosy@kp.org
Jay Kim - Reserach Assistant, BS, jay.x.kim@kp.org
About this study
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:
- 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.
- 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.
- Being tested
- AI-Enabled Identification (EchoNet-Liver)
- Medical subject headings
- Fibrosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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 group | Role | What they receive |
|---|---|---|
| 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 for standard of care at their site. Receives: Other: AI-Enabled Identification (EchoNet-Liver) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: From enrollment to end of follow up at 1 year.
Secondary outcomes (11)
Time to diagnosis of MASLD/cirrhosis
Time (days) from AI identification to first confirmatory diagnosis
Measured: Followed up to 24 months post notification.
Time to diagnosis for MASLD with F2 fibrosis or greater
Time (days) from AI identification to first confirmatory diagnosis
Measured: Followed up to 24 months post notification.
Time to diagnosis for steatotic liver disease
Time (days) from AI identification to first confirmatory diagnosis
Measured: Followed up to 24 months post notification.
Time to confirmatory imaging
Time (days) from AI identification
Measured: Followed up to 24 months post notification.
Time to initiation of targeted treatment
Time (days) from AI identification
Measured: Followed up to 24 months post notification.
All-cause mortality
Time (days) from AI identification
Measured: Followed up to 24 months post notification.
All-cause hospitalization
Time (days) from AI identification
Measured: Followed up to 24 months post notification.
Heart failure hospitalization
Time (days) from AI identification (Defined as admission with IV diuretics or elevated BNP)
Measured: Followed up to 24 months post notification.
Cardiovascular hospitalization
Time (days) from AI identification for Cardiovascular hospitalization (defined by principal ICD9/10 code)
Measured: Followed up to 24 months post notification.
Hepatic decompensation hospitalization
Time (days) from AI identification for hepatic decompensation hospitalization (defined by ascites, hepatic encephalopathy, variceal bleeding, hepatocellular carcinoma, or liver transplantation)
Measured: Followed up to 24 months post notification.
New ASCVD diagnosis
Time (days) from AI identification
Measured: Followed up to 24 months post notification.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Oct 2025
- Study start1 Jan 2028 (estimated)
- Primary completion1 Nov 2028 (estimated)
- Study completion1 Nov 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for MASLD - Metabolic Dysfunction-Associated Steatotic Liver DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Kaiser Permanente
- Sponsor type
- University, hospital or other
- Collaborators
- Stanford University, Massachusetts General Hospital, Cedars-Sinai Medical Center
- Responsible party
- Principal investigator, David Ouyang, Kaiser Permanente
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07216859
- Sponsor's protocol ID
- 2356334
- Other IDs
- 25AHAAI1487693
- Acronym
- SCOUT Echo-AI
- Status last verified
- Aug 2026
- First submitted
- 13 Oct 2025
- Last updated
- 13 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 13 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Screening Cardiometabolic Opportunities Using Transformative Echocardiography Artificial Intelligence (SCOUT Echo-AI)
- ClinicalTrials.gov ID
- NCT07216859
- Acronym / sponsor ID
- SCOUT Echo-AI / 2356334
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Kaiser PermanenteUniversity, hospital or other
- Collaborators
- Stanford University, Massachusetts General Hospital, Cedars-Sinai Medical Center
- Study start
- 1 Jan 2028 (estimated)
- Primary completion
- 1 Nov 2028 (estimated)
- Study completion
- 1 Nov 2028 (estimated)
- First posted
- 15 Oct 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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.
- 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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI Notification (EchoNet-Liver-Flagged patients) | Experimental | Other: AI-Enabled Identification (EchoNet-Liver)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 for standard of care at their site. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Enabled Identification (EchoNet-Liver)AI-generated notifications to clinicians about possible undiagnosed liver disease (MASLD and/or Cirrhosis) detected from Transthoracic Echocardiogram | Other | AI Notification (EchoNet-Liver-Flagged patients) |
Primary outcome
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).
11 secondary outcomes
Time to diagnosis of MASLD/cirrhosis
Time frame: Followed up to 24 months post notification.
Time to diagnosis for MASLD with F2 fibrosis or greater
Time frame: Followed up to 24 months post notification.
Time to diagnosis for steatotic liver disease
Time frame: Followed up to 24 months post notification.
Time to confirmatory imaging
Time frame: Followed up to 24 months post notification.
Time to initiation of targeted treatment
Time frame: Followed up to 24 months post notification.
All-cause mortality
Time frame: Followed up to 24 months post notification.
All-cause hospitalization
Time frame: Followed up to 24 months post notification.
Heart failure hospitalization
Time frame: Followed up to 24 months post notification.
Cardiovascular hospitalization
Time frame: Followed up to 24 months post notification.
Hepatic decompensation hospitalization
Time frame: Followed up to 24 months post notification.
New ASCVD diagnosis
Time frame: Followed up to 24 months post notification.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Aug 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
6 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Enabled Identification (EchoNet-Liver) in people with MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease and Cirrhosis, run by Kaiser Permanente at 6 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 13 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the 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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Aug 2026.