AI Echocardiographic Screening for Cardiac Amyloidosis

Enrolling by invitation at 3 trial locations

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Cedars-Sinai Medical Center

Disqualifiers: Decline consent, Not suspicious echocardiogram

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 use artificial intelligence (AI) to enhance the detection of cardiac amyloidosis (CA), a rare heart condition often mistaken for other heart diseases. The EchoNet-LVH algorithm analyzes echocardiograms (heart ultrasound images) to identify signs of CA that human experts might miss. Participants with echocardiograms flagged as suspicious by this AI tool qualify for further evaluation. Early detection is crucial, as timely treatment can significantly improve the quality of life for those with CA. As an unphased trial, this study allows participants to contribute to groundbreaking research in early disease detection.

Will I have to stop taking my current medications?

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

What prior data suggests that this AI echocardiographic screening method is safe for cardiac amyloidosis detection?

Research has shown that EchoNet-LVH, an AI tool, detects signs of cardiac amyloidosis (CA) by analyzing heart images from echocardiograms. This tool employs advanced computer techniques to enhance the accuracy of identifying CA, a serious heart condition that often goes unnoticed.

Although specific safety data for EchoNet-LVH is not provided, it is important to note that this tool is neither a drug nor a treatment that enters the body. It is a computer program that analyzes images to assist doctors in making better decisions. Since it does not directly interact with the body, it is considered safe for screening patients.

The algorithm aims to improve the detection process, reducing human error and potentially identifying the disease earlier. This can lead to earlier treatments with existing therapies, which have been shown to improve patient outcomes. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the AI Echocardiographic Screening for Cardiac Amyloidosis because it uses the EchoNet-LVH algorithm, a cutting-edge AI-enhanced echocardiogram review tool. Unlike traditional methods that rely heavily on manual interpretation and can be time-consuming, this AI approach aims to quickly and accurately identify patients who may have cardiac amyloidosis. The EchoNet-LVH algorithm offers the potential for earlier detection and intervention by automating the initial screening process, which could lead to improved outcomes for patients. This innovative use of AI in cardiology represents a significant advancement in how we approach diagnosing complex heart conditions.

What evidence suggests that the EchoNet-LVH algorithm is effective for screening cardiac amyloidosis?

Studies have shown that EchoNet-LVH, the investigational tool evaluated in this trial, effectively identifies cardiac amyloidosis (CA), a rare heart condition. Research indicates that this AI tool aids in making earlier and more accurate diagnoses by analyzing heart ultrasounds, known as echocardiograms. EchoNet-LVH proves especially useful because it detects features that human experts might miss. International testing has demonstrated its effectiveness across diverse populations. By identifying CA early, EchoNet-LVH helps patients receive appropriate treatment sooner, potentially reducing health problems and extending life expectancy. ^{3 4 6 7 8}

Who Is on the Research Team?

Lily Stern, MD

Principal Investigator

Cedars-Sinai Medical Center

Are You a Good Fit for This Trial?

This trial is for individuals who may have cardiac amyloidosis, a rare heart condition often mistaken for other types of heart failure. It's especially aimed at those with symptoms or conditions that could be related to this disease and would typically undergo echocardiography.

Inclusion Criteria

Patients receiving an echocardiogram that is determined to be suspicious by EchoNet-LVH

Exclusion Criteria

I have chosen not to give my consent for participation.

Patients receiving an echocardiogram that is determined to be not suspicious by EchoNet-LVH

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

AI-Enhanced Echocardiogram Review

Potential participants are identified by automated AI-enhanced echocardiogram review and chart reviewed by CA experts for enrollment appropriateness

4-6 weeks

Follow-up

Participants are monitored for confirmation of cardiac amyloidosis and other outcomes

1 year

What Are the Treatments Tested in This Trial?

Interventions

• EchoNet-LVH

Trial Overview The trial is testing an AI algorithm called EchoNet-LVH designed to improve the detection of cardiac amyloidosis using routine echocardiogram images. The goal is to see if this technology can more accurately identify patients who need further screening.

How Is the Trial Designed?

Group I: Suspicious by EchoNet-LVH Algorithm Experimental Treatment 1 Intervention

Each potential participant identified by automated AI-enhanced echocardiogram review will be chart reviewed by each site's CA experts for appropriateness of enrollment and clinican suspicion for CA. Based on the judgement of CA experts, potential participants that meet eligibility criteria will be called to be consented, followed in the study, and referred to see the CA expert.

Who Is Running the Clinical Trial?

Cedars-Sinai Medical Center

Lead Sponsor

Trials

523

Recruited

165,000+

Palo Alto Veteran Affairs Hospital

Collaborator

Trials

1

Recruited

500+

Providence Heart & Vascular Institute

Collaborator

Trials

2

Recruited

620+

Northwestern Medicine

Collaborator

Trials

14

Recruited

9,500+

Published Research Related to This Trial

A study involving 138 patients (74 with cardiac amyloidosis and 64 with hypertrophic cardiomyopathy) demonstrated that machine learning models, particularly random forest and gradient boosting, can effectively differentiate between these two conditions with high accuracy (AUC up to 0.98).

The use of machine learning combined with speckle tracking echocardiography shows promise in improving the timely diagnosis of cardiac amyloidosis, which is often misdiagnosed, potentially leading to better patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine learning algorithms to automate differentiating cardiac amyloidosis from hypertrophic cardiomyopathy. Wu, ZW., Zheng, JL., Kuang, L., et al.[2023]

Citations

1. ahajournals.org ahajournals.org/doi/10.1161/circ.150.suppl_1.4146940

Abstract 4146940: External Validation of EchoNet-LVH, a ... Results: EchoNet-LVH score could be calculated in 130 out of 204 patients (64%). Mean age was 55 years, 28% were female, and 52 were defined as ...

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11702730/

International Validation of Echocardiographic AI Amyloid ... Conclusion. EchoNet-LVH can assist with earlier and accurate diagnosis of CA. As CA is a rare disease, EchoNet-LVH is highly specific in order ...

3. sciencedirect.com sciencedirect.com/science/article/pii/S2772963X25003217

Evaluating the Performance and Potential Bias of ... EchoNet-LVH generates a prediction for likelihood of cardiac amyloidosis (any type) from an echocardiogram study synthesizing information about wall thickness ...

4. medrxiv.org medrxiv.org/content/10.1101/2024.10.09.24315202v1.full.pdf

Evaluating the performance and potential bias of predictive ... The Mayo ATTR-CM Score and EchoNet-LVH models demonstrated similar performance in our validation cohort as compared to their initially reported ...

5. echonet.github.io echonet.github.io/lvh/

EchoNet LVH The EchoNet-LVH dataset includes 12,000 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a ...

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

Value of Artificial Intelligence for Enhancing Suspicion ... Untreated AL amyloidosis is a medical urgency, with one retrospective study demonstrating that sudden death occurred in 29% of all AL deaths.

7. clinicaltrials.gov clinicaltrials.gov/study/NCT06664866

AI Echocardiographic Screening of Cardiac Amyloidosis The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional ...

8. withpower.com withpower.com/trial/phase-amyloidosis-9-2024-10e74

AI Echocardiographic Screening for Cardiac Amyloidosis The research articles provided do not contain specific safety data ... What data supports the effectiveness of the treatment EchoNet-LVH for cardiac amyloidosis?