AI-Enhanced ECG for Cardiac Amyloidosis

Not currently recruiting at 8 trial locations

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Mayo Clinic

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 aims to test a new AI tool that helps doctors better detect cardiac amyloidosis, a condition where abnormal proteins build up in the heart. By using this AI-enhanced ECG, the researchers aim to improve the diagnosis of this heart condition. The trial consists of two parts: one where doctors receive AI-generated alerts and one where they do not. Only Mayo Clinic cardiology or hematology providers who agree to join the study can participate.

As an unphased study, this trial offers a unique opportunity to contribute to cutting-edge research that could enhance future cardiac care.

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

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

What prior data suggests that this AI-enabled ECG screening tool is safe for diagnosing cardiac amyloidosis?

Research has shown that artificial intelligence (AI) tools, such as the AI ECG Amyloid algorithm, are being explored to identify heart conditions more effectively. This tool aims to improve the diagnosis of cardiac amyloidosis (CA), a condition where abnormal proteins accumulate in the heart.

AI tools have been studied for their ability to analyze electrocardiograms (ECGs) and other heart data, aiding doctors in early detection of heart issues. The U.S. Food and Drug Administration (FDA) has approved over 500 AI algorithms for use in medical imaging, supporting the idea that AI can be safe and reliable.

Although specific safety data for the AI ECG Amyloid algorithm is not detailed, the approval of similar AI tools for medical use suggests a level of safety and trust. This may provide potential trial participants with some confidence in the AI ECG Amyloid algorithm's safety as it undergoes further evaluation in clinical settings. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the AI ECG Amyloid algorithm because it could revolutionize how cardiac amyloidosis is detected. Unlike traditional diagnostic methods that rely on symptoms and extensive testing, this AI-driven approach analyzes ECG data to provide early alerts and recommendations to healthcare providers. This means potentially faster diagnosis and treatment, which could significantly improve patient outcomes. By integrating AI technology, this method promises to enhance the accuracy and efficiency of cardiac care, setting it apart from current practices.

What evidence suggests that this AI ECG algorithm is effective for diagnosing cardiac amyloidosis?

Research has shown that AI-enhanced ECG tools effectively spot cardiac amyloidosis, a heart condition caused by protein buildup. Studies have found that these AI tools help identify the disease early, which is crucial for timely treatment. In this trial, participants in one arm will receive notifications of the AI ECG algorithm and the A3E scores, which include a provider-facing recommendation report for positive AI ECG Amyloid Scores. This tool accurately indicates disease activity and can track treatment effectiveness. By analyzing ECG data with advanced technology, this tool offers a promising way to detect cardiac amyloidosis sooner than traditional methods. Early findings suggest that using AI in this way could greatly improve the diagnosis and management of the condition. ^{1 2 3 6 7}

Who Is on the Research Team?

Angela Dispenzieri, MD

Principal Investigator

Mayo Clinic

Are You a Good Fit for This Trial?

This trial is for Mayo Clinic providers who agree to participate and are involved in caring for adult patients within cardiology or hematology departments. There are no specific exclusion criteria, making it broadly accessible to these healthcare professionals.

Inclusion Criteria

Mayo Clinic providers who consent to participate on this study

I am under the care of a Mayo Clinic cardiologist or hematologist.

Exclusion Criteria

N/A

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Providers receive AI ECG algorithm and educational guidance to improve diagnosis of cardiac amyloidosis

1 year

Follow-up

Participants are monitored for safety and effectiveness after intervention

1 year

What Are the Treatments Tested in This Trial?

Interventions

• AI ECG Amyloid algorithm

Trial Overview The study is evaluating an innovative AI-powered tool that analyzes ECG readings to help detect cardiac amyloidosis more effectively. This could potentially improve diagnosis rates of this heart condition.

How Is the Trial Designed?

Group I: Notification of the AI ECG algorithm and the A3E scores Experimental Treatment 1 Intervention

Provider-facing recommendation report that alerts to provider for positive AI ECG Amyloid Score, standardized amyloid order set, diagnostic algorithm and reminders

Group II: Usual Care Active Control 1 Intervention

No alerts to provider for positive AI ECG Amyloid Score, standardized amyloid order set, diagnostic algorithm and reminders

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials

3,427

Recruited

3,221,000+

Published Research Related to This Trial

The study utilized machine learning algorithms to analyze cardiac strain and function data from 43 patients with cardiac amyloidosis, achieving a high diagnostic accuracy of 90.9% using the SVM RBF kernel, indicating its potential for improving CA diagnostics.

Key predictors for cardiac amyloidosis were identified, including bi-atrial longitudinal strain and left and right atrial ejection fraction, suggesting that these parameters could enhance non-invasive diagnostic approaches in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function. Eckstein, J., Moghadasi, N., Körperich, H., et al.[2022]

Citations

1. jacc.org jacc.org/doi/10.1016/j.jaccas.2024.102968

Artificial Intelligence-Enhanced Electrocardiogram: A ... Take-Home Messages. •. AI-ECG accurately reflects disease activity in cardiac amyloidosis and can be used to monitor therapeutic response.

2. ahajournals.org ahajournals.org/doi/10.1161/JAHA.124.036533

Value of Artificial Intelligence for Enhancing Suspicion ... This narrative review of published literature describes the performance of AI tools that use data from ECGs and echocardiography to improve identification of ...

3. clinicaltrials.gov clinicaltrials.gov/study/NCT05557162

NCT05557162 | Artificial Intelligence Enhanced ECG to ... The purpose of this study is to assess a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for improving the diagnosis of ...

4. arci.org arci.org/wp-content/uploads/2025/06/Artificial-intelligence-in-cardiac-amyloidosis-Expert-insights-on-adoption-challenges-opportunities-and-best-practices-for-developing-effective-tools.pdf

Artificial intelligence in cardiac amyloidosis: Expert insights ... Recent advances in artificial intelligence (AI) offer significant potential to address the diagnostic challenges of cardiac amyloidosis (CA) by ...

5. sciencedirect.com sciencedirect.com/science/article/pii/S2666084924010325

Artificial Intelligence-Enhanced Electrocardiogram: A ... AI-ECG has emerged as a promising tool capable of detecting cardiac amyloidosis, potentially facilitating early diagnosis.

6. cancer.gov cancer.gov/research/participate/clinical-trials-search/v?id=NCI-2024-00928

Artificial Intelligence Enhanced ECG Screening Tool for ... The AI-enabled ECG screening tool uses an algorithm to identify those at greatest risk for a CA diagnosis and generates a report with recommendations for follow ...

7. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11383475/

Tracking the Preclinical Progression of Transthyretin ... AI-enabled echocardiography and electrocardiography may enable scalable risk stratification of ATTR-CM during its pre-clinical course. Keywords: ...