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

NCT05139797 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2025-06-27

No results posted yet for this study

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

Interventions

OTHER

EchoNet-LVH screening for cardiac amyloidosis

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.

Sponsors & Collaborators

  • Cedars-Sinai Medical Center

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-11-18
Primary Completion
2026-01-01
Completion
2027-06-01
FDA Device
Yes

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05139797 on ClinicalTrials.gov