AI-Enabled Identification for Fatty Liver Disease

(SCOUT Echo-AI Trial)

Not yet recruiting at 3 trial locations

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Kaiser Permanente

Disqualifiers: Inability to consent, Hospice, Transplant, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new AI tool, EchoNet-Liver, designed to catch liver disease early. It aims to determine if using AI during heart ultrasounds can accelerate diagnosis and treatment for liver conditions like fatty liver disease or cirrhosis. Participants who recently had a heart ultrasound and are flagged by the AI as high risk for liver issues may be suitable candidates. The study will compare the speed and effectiveness of treatment for these flagged patients versus those who do not use the AI tool. As an unphased trial, this study allows participants to contribute to innovative research that could lead to faster and more accurate liver disease diagnosis.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What prior data suggests that this AI-augmented echocardiography screening approach is safe for early detection of liver disease?

Research has shown that EchoNet-Liver, an AI tool for detecting liver problems, is promising in identifying conditions like fatty liver disease and cirrhosis. Studies have found that this AI effectively highlights areas of concern in the liver during heart ultrasounds. Since it is not a drug or physical treatment, there is no direct evidence of safety issues with this AI tool. Instead, it assists doctors by identifying potential liver problems for further testing. So far, no reports of negative effects or harm have emerged from using the AI in these screenings, suggesting that the tool is safe and well-tolerated in these situations. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about EchoNet-Liver because it uses artificial intelligence to identify patients at high risk for metabolic-associated steatotic liver disease (MASLD) and cirrhosis by analyzing transthoracic echocardiograms. Unlike the standard approach, which often relies on invasive liver biopsies or less accessible imaging techniques like MRI, EchoNet-Liver offers a non-invasive, efficient, and potentially more accessible method for early detection. By flagging high-risk patients, this AI model can streamline the diagnostic process and ensure timely intervention, which could significantly improve patient outcomes.

What evidence suggests that this AI-augmented echocardiography screening approach is effective for early detection of liver disease?

Studies have shown that EchoNet-Liver, an AI tool, can effectively identify chronic liver diseases like fatty liver disease and cirrhosis. Research indicates that this AI model can spot patients at high risk by analyzing images from heart ultrasounds (echocardiograms). In this trial, if EchoNet-Liver flags a participant's echocardiogram as high risk for MASLD and/or cirrhosis, a notification will be sent to their primary treating clinician, or the participant will undergo a structured diagnostic workflow. Previous comparisons have shown that EchoNet-Liver accurately identifies problem areas in the liver, aiding in early detection. Early identification can lead to faster diagnosis and treatment, which is crucial for managing liver diseases. This promising technology could improve patient outcomes by catching problems sooner than traditional methods. ^{1 2 3 4 6}

Are You a Good Fit for This Trial?

This trial is for patients undergoing routine heart ultrasounds who may be at risk of liver diseases like fatty liver or cirrhosis. It's not clear what excludes someone from participating, but typically, people with conditions that could interfere with the study or its results might not be eligible.

Inclusion Criteria

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

I am 18 years old or older.

Exclusion Criteria

I am unable to give consent or communicate my health decisions.

I have had a liver or kidney transplant.

Enrollment in hospice or life expectancy so limited that additional evaluation would not be appropriate per clinician judgment

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

AI Notification and Diagnostic Workflow

Participants whose echocardiograms are flagged by AI as high risk for MASLD and/or cirrhosis receive notifications and undergo a structured diagnostic workflow

Up to 12 months

Follow-up

Participants are monitored for clinical outcomes such as time to diagnosis, treatment uptake, and hospitalizations

24 months

What Are the Treatments Tested in This Trial?

Interventions

• EchoNet-Liver

Trial Overview The SCOUT Echo-AI trial is testing an AI tool called EchoNet-Liver during regular heart scans to see if it can spot early signs of serious liver problems quicker than usual care. The study will check if this leads to faster diagnosis and treatment.

How Is the Trial Designed?

Group I: AI Notification (EchoNet-Liver-Flagged patients) Experimental Treatment 1 Intervention

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.

Who Is Running the Clinical Trial?

Kaiser Permanente

Lead Sponsor

Trials

563

Recruited

27,400,000+

Stanford University

Collaborator

Trials

2,527

Recruited

17,430,000+

Massachusetts General Hospital

Collaborator

Trials

3,066

Recruited

13,430,000+

Cedars-Sinai Medical Center

Collaborator

Trials

523

Recruited

165,000+

Citations

1. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12493085/

Opportunistic Screening of Chronic Liver Disease with ... Comparison of EchoNet-Liver performance in cohorts with different disease ... Diagnosis and management of nonalcoholic fatty liver disease. JAMA ...

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11213089/

Opportunistic Screening of Chronic Liver Disease with ... ... Cirrhosis, Steatotic Liver Disease, Non-Alcoholic Fatty Liver Disease ... For both cirrhosis and SLD, EchoNet-Liver highlighted the liver of the subcostal ...

3. medrxiv.org medrxiv.org/content/10.1101/2024.06.13.24308898v1

Opportunistic Screening of Chronic Liver Disease with ... proton density fat fraction. Copyright. The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to ...

4. github.com github.com/echonet/liver

echonet/liver Echo-Net-Liver, a deep-learning algorithm pipeline, is developed to identify chronic liver disease (particularly steatotic liver disease (SLD) and cirrhosis), ...

5. fmai-hub.com fmai-hub.com/ai-powered-fatty-liver-disease-monitoring-helps-tackle-growing-global-burden/

AI-Powered Fatty Liver Disease Monitoring Helps Tackle ... ... fatty liver disease, is characterized by excessive fat accumulation in the liver. ... Liver, tools such as EchoNet-Liver could provide an ...

6. researchgate.net researchgate.net/publication/381446116_Opportunistic_Screening_of_Chronic_Liver_Disease_with_Deep_Learning_Enhanced_Echocardiography

(PDF) Opportunistic Screening of Chronic Liver Disease ... Global burden of non-alcoholic fatty liver disease in 399. 204 countries and territories from 1990 to 2019. Clin Res Hepatol Gastroenterol ...