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

Recruiting now

Conditions studied: Cardiac Amyloidosis

In brief

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.

Key facts

Study ID

NCT05139797

Run by

Cedars-Sinai Medical Center

People needed

300

Starts

2021-11-18

Expected to finish

2027-06-01

Last updated by the study team

2026-07-22

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

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

Where it is running

• Cedars-Sinai Medical Centre (Los Angeles) — Los Angeles, California, United States (enrolling)

Full record on ClinicalTrials.gov

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