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Artificial intelligence screens echocardiograms to flag cardiac amyloidosis

Tests an AI tool reading heart ultrasounds to flag adults for cardiac amyloidosis screening.

Plain-language title generated from the registry record
Registry title: Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases (EchoNet-Screening)

Key facts

Where
Los Angeles, United StatesSee the site

Where in United States

  • Cedars-Sinai Medical Centre (Los Angeles)Los Angeles, United StatesTaking people

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Trial details

Who runs it
Cedars-Sinai Medical Center
Trial code
NCT05139797
Record updated
22 July 2026
Original description
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.Read full original descriptionShow less

Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases

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.

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

Exclusion: Patients who decline to be seen at specialty clinic; Patients who have passed away.

ClinicalTrials.gov NCT05139797