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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

2000 participants

Study Classification

INTERVENTIONAL

Study Start Date

2026-01-01

Study Completion Date

2027-11-01

Brief Summary

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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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Detailed Description

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Conditions

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MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease Cirrhosis

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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AI Notification (EchoNet-Liver-Flagged patients)

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.

Group Type EXPERIMENTAL

AI-Enabled Identification (EchoNet-Liver)

Intervention Type OTHER

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

Interventions

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AI-Enabled Identification (EchoNet-Liver)

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

Intervention Type OTHER

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No