AI Echocardiographic Screening of Cardiac Amyloidosis

Part of paid clinical trials in Los Angeles, California.

Sponsor
Cedars-Sinai Medical Center
Study ID
NCT06664866
Status
Enrolling By Invitation

Conditions

  • Cardiac Amyloidosis

Eligibility Criteria

Sex
ALL
Age
22 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • EchoNet-LVH Assessment — DIAGNOSTIC_TEST
    The AI algorithm is previously described (Duffy et al. JAMA Cardiology 2022) and will remain unchanged throughout the course of the study. A pre-determined threshold based on prior experiments and analysis has been decided prior to the study. From each site, approximately 100,000 echocardiogram studies will be reviewed by EchoNet-LVH for approximately 500 patients to be flagged.

Study Details

Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and accurately assess common measurements made in clinical practice. Echocardiography is the most common form of cardiac imaging and is routinely and frequently used for diagnosis. However, there is often subjectivity and heterogeneity in interpretation. Artificial intelligence (AI)'s ability for precision measurement and detection is important in both disease screening as well as diagnosis of cardiovascular disease. Cardiac amyloidosis (CA) is a rare, underdiagnosed disease with targeted therapies that reduce morbidity and increase life expectancy. However, CA is frequently overlooked and confused with heart failure with preserved ejection fraction. Some estimates suggest that CA can be as prevalence as 1% in a general population, with even higher prevalence in patients with left ventricular hypertrophy, heart failure, and other cardiac symptoms that might prompt echocardiography. AI guided disease screening workflows have been proposed for rare diseases such as cardiac amyloidosis and other diseases with relatively low prevalence but significant human impact with targeted therapies when detected early. This is an area particularly suitable for AI as there are multiple mimics where diseases like hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, and other phenotypes might visually be similar but can be distinguished by AI algorithms. The investigators have developed an algorithm, termed EchoNet-LVH, to identify cardiac hypertrophy and identify patients who would benefit from additional screening for cardiac amyloidosis.

Key Dates

Start date
Oct 28, 2024
Status verified
Jun 2025
Primary completion
Nov 1, 2025
Completion
Nov 1, 2026

Study Design

Enrollment
500 participants (estimated)
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC

Arms

  • Experimental: Suspicious by EchoNet-LVH Algorithm
    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.

Primary Outcome Measure

Positive Predictive Value [ Time Frame: 1 year ]

Locations (4)

FacilityCityStateZIPSite coordinators
Cedars Sinai Medical CenterLos AngelesCalifornia90034-
Palo Alto Veteran Affairs HospitalPalo AltoCalifornia94304-
Northwestern MedicineChicagoIllinois60190-
Providence Heart and Vascular InstitutePortlandOregon97225-

Find similar trials in Los Angeles, CA

By research site
Cedars Sinai Medical Center· Los Angeles, CAPalo Alto Veteran Affairs Hospital· Palo Alto, CANorthwestern Medicine· Chicago, ILProvidence Heart and Vascular Institute· Portland, OR

Related Studies