Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases (EchoNet-Screening)

NCT05139797 | Recruiting FDA Device

• 1 other identifier: STUDY00001720
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief Summary

Despite rapidly advancing developments in targeted therapeutics and genetic sequencing, persistent limits in the accuracy and throughput of clinical phenotyping has led to a widening gap between the potential and the actual benefits realized by precision medicine. Recent advances in machine learning and image processing techniques have shown that machine learning models can identify features unrecognized by human experts and more precisely/accurately assess common measurements made in clinical practice. 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 and will prospectively evaluate its accuracy in identifying patients whom would benefit from additional screening for cardiac amyloidosis.

Conditions

Cardiac Amyloidosis

Keywords

Echocardiogram; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• Number of New Diagnoses of Cardiac Amyloidosis Found

  From chart review, identification of patients who have a downstream diagnosis of cardiac amyloidosis

  6 months

Secondary Outcomes (2)

• Number of New Diagnoses of TTR Amyloidosis Found

  6 months

• Number of New Diagnoses of AL Amyloidosis Found

  6 months

Study Arms (1)

Artificial Intelligence Screening for Cardiac Amyloidosis

An artificial intelligence algorithm will produce a probability of cardiac amyloidosis that will trigger referral to specialty clinic for further evaluation.

Other: EchoNet-LVH screening for cardiac amyloidosis

Interventions

EchoNet-LVH screening for cardiac amyloidosis OTHER

An AI algorithm identifies LVH, low voltage, and high suspicion for cardiac amyloidosis. The intervention is the suspicion score. Patients with high suspicion score will be referred to specialty clinic for standard of care evaluation, screening, and treatment as determined by physicians.

Artificial Intelligence Screening for Cardiac Amyloidosis

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients who have a high suspicion for cardiac amyloidosis by AI algorithm

You may qualify if:

• Patients who have a high suspicion for cardiac amyloidosis by AI algorithm

You may not qualify if:

• Patients who decline to be seen at specialty clinic
• Patients who have passed away

Sponsors & Collaborators

• Cedars-Sinai Medical Center: lead

Study Sites (1)

Cedars-Sinai Medical Centre (Los Angeles)

Los Angeles, California, 90048, United States

RECRUITING

Related Links

• Related Info

MeSH Terms

Conditions

Amyloid Neuropathies, Familial

Condition Hierarchy (Ancestors)

Heredodegenerative Disorders, Nervous System; Neurodegenerative Diseases; Nervous System Diseases; Amyloid Neuropathies; Peripheral Nervous System Diseases; Neuromuscular Diseases; Genetic Diseases, Inborn; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Amyloidosis, Familial; Metabolism, Inborn Errors; Metabolic Diseases; Nutritional and Metabolic Diseases; Amyloidosis; Proteostasis Deficiencies

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Staff Physician

Study Record Dates

• First Submitted: November 18, 2021
• First Posted: December 1, 2021
• Study Start: November 18, 2021
• Primary Completion: January 1, 2026
• Study Completion (Estimated): June 1, 2027
• Last Updated: June 27, 2025

Record last verified: 2025-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)