AI-Enabled ECG for Liver Disease

(ADVANCE Trial)

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Mayo Clinic

Disqualifiers: Cirrhosis

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 tool, ACE 2.0, which uses AI (artificial intelligence) to detect early signs of serious liver problems. The goal is to determine if this tool can help doctors identify liver disease early and assess its ease of use during regular doctor visits. The trial includes two groups—one receives usual care, while the other uses the AI tool to evaluate its impact. Adults who require an ECG (a heart test) as part of their usual care may be suitable for this trial, provided they do not have a known liver disease. As an unphased trial, this study offers a unique opportunity to contribute to innovative research that could enhance early detection of liver disease.

Do I need to stop my current medications for this trial?

The trial protocol does not specify whether you need to stop taking your current medications.

What prior data suggests that the ACE 2.0 model is safe for use in detecting liver disease?

Research has shown that the ACE 2.0 model is a computer tool used to detect liver problems. It accurately identifies cirrhosis, a serious liver disease. This tool aids doctors in predicting liver issues in patients and has been successfully applied in transplant situations.

No negative effects or safety concerns have been reported from using this AI tool. As it is a tool and not a medicine, it does not interact directly with the body, which generally indicates safety. However, always consult your doctor about any concerns before joining a trial. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it explores a novel way to use AI in diagnosing advanced liver disease. Unlike traditional methods that rely heavily on blood tests and imaging, the ACE 2.0 system uses an AI-enhanced electrocardiogram (ECG) to detect signs of liver issues. This technology could potentially alert doctors sooner and recommend follow-up tests, offering a quicker, non-invasive approach. By leveraging AI, this method may improve early detection and treatment options, potentially leading to better outcomes for patients.

What evidence suggests that the ACE 2.0 model is effective for early detection of advanced liver fibrosis?

Research has shown that the ACE 2.0 model, which participants in this trial may experience in the Electrocardiogram AI Group, effectively identifies liver problems. It uses signals from an ECG, a heart test, to detect issues related to cirrhosis, a serious liver condition. Studies indicate that the ACE score can accurately predict liver-related problems, such as worsening liver function, and helps doctors better understand the risk of severe liver disease. This AI tool could greatly aid in the early detection and management of liver disease. ^{1 3 6 7 8}

Who Is on the Research Team?

Douglas Simonetto, MD

Principal Investigator

Mayo Clinic

Are You a Good Fit for This Trial?

This trial is for adults who are getting an ECG test and whose doctors can order such tests. It's aimed at those in primary care settings, including physicians, nurse practitioners, and physician assistants who agree to participate. People with known advanced liver disease or a history of cirrhosis are not eligible.

Inclusion Criteria

You are attended to by a primary healthcare provider such as a doctor, nurse practitioner or physician assistant.

Information will be gathered from patients' EMRs.

You possess the capacity to order an electrocardiogram (ECG).

Exclusion Criteria

Patients with known cirrhosis based on noninvasive fibrosis assessment tests, liver biopsy or complications of decompensated disease, or with a documented history of cirrhosis identified by clinical notes

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

The ACE 2.0 model is used to alert providers to the likelihood of advanced liver disease with a recommendation for a FibroTest-ActiTest

6 months

Follow-up

Participants are monitored for safety and effectiveness after the intervention

6 months

What Are the Treatments Tested in This Trial?

Interventions

• ACE 2.0

Trial Overview The study is testing the ACE (AI-Cirrhosis-ECG) 2.0 model to see if it can effectively detect early signs of severe liver fibrosis using AI analysis of ECG results. The goal is also to assess how well this AI tool is accepted in a primary care environment.

How Is the Trial Designed?

Group I: Electrocardiogram AI Group Experimental Treatment 1 Intervention

The ACE (AI-Cirrhosis-ECG) 2.0 will be used to alert primary care providers to the likelihood of advanced liver disease with a recommendation for laboratory tests.

Group II: Usual Care Group Active Control 1 Intervention

Primary care providers will treat subject per standard of care

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials

3,427

Recruited

3,221,000+

Published Research Related to This Trial

A study involving 44,959 patients demonstrated that an AI model applied to ECG data can effectively identify asymptomatic left ventricular dysfunction (ALVD), achieving high accuracy (85.7%) and sensitivity (86.3%).

Patients who screened positive for ALVD using the AI model were found to be four times more likely to develop future ventricular dysfunction, highlighting the potential of AI-enhanced ECG as a proactive screening tool for early intervention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Attia, ZI., Kapa, S., Lopez-Jimenez, F., et al.[2022]

The AI-ECG algorithm demonstrated strong performance in detecting left ventricular systolic dysfunction (LVSD) in an external population of 4277 adults, achieving an area under the receiver operating curve of 0.82, indicating good accuracy.

While the AI-ECG showed high specificity (97.4%) and accuracy (97.0%), its sensitivity was lower at 26.9%, suggesting that population-specific cut-offs may be needed for optimal clinical use, especially given the differences in patient characteristics compared to the original study.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. Attia, IZ., Tseng, AS., Benavente, ED., et al.[2021]

Citations

1. sciencedirect.com sciencedirect.com/science/article/pii/S2589555925000321

AI-Cirrhosis-ECG (ACE) score for predicting ... The ACE score accurately identifies hepatic decompensation and independently predicts liver-related outcomes in cirrhosis.

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC9727935/

Development of the AI-Cirrhosis-ECG (ACE) Score This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG.

3. journals.lww.com journals.lww.com/ajg/fulltext/2025/10000/training_and_performance_of_an.39.aspx

Training and Performance of an Electrocardiogram ... The development of the ACE (AI-Cirrhosis-ECG) score (9) showed that signals captured by 12-lead ECGs contain information to distinguish if a patient has end ...

4. mayoclinic.org mayoclinic.org/medical-professionals/transplant-medicine/news/ai-model-improves-risk-stratification-for-liver-transplant-patients/mac-20589594

AI model improves risk stratification for liver transplant ... A new ECG-based machine learning model is giving transplant surgeons a better predictor of severe liver disease and a tool for risk ...

5. pubmed.ncbi.nlm.nih.gov pubmed.ncbi.nlm.nih.gov/40276480/

AI-Cirrhosis-ECG (ACE) score for predicting decompensation ... The ACE score accurately identifies hepatic decompensation and independently predicts liver-related outcomes in cirrhosis.

6. mayo.edu mayo.edu/research/labs/data-analytics-ai-advanced-liver-disease/research

Data Analytics and AI for Advanced Liver Disease - Research ECG-based detection of cirrhosis using the AI-Cirrhosis-ECG (ACE) score. This deep-learning model can accurately detect the presence of cirrhosis and grade the ...

7. mayo.edu mayo.edu/research/clinical-trials/cls-20547081

A Study to Detect Advanced Liver Disease via AI-enabled ... The overall objectives of this study are to determine the effectiveness of ACE 2.0 model in early detection of advanced liver fibrosis, and to determine the ...

8. beckershospitalreview.com beckershospitalreview.com/healthcare-information-technology/ai/mayo-clinic-ai-model-improves-liver-transplant-risk-scores/

Mayo Clinic AI model improves liver transplant risk scores Findings from the AI-cirrhosis-ECG model, or ACE, were published in JHEP Reports in 2024 and highlighted by Mayo Clinic Sept. 30. Researchers ...