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

Study 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. 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.

Primary Outcome

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

Interventions

• OTHER AI-Enabled Identification (EchoNet-Liver)