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Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases (EchoNet-Screening)
Sponsor: Cedars-Sinai Medical Center
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.
Official title: Artificial Intelligence Guided Echocardiographic Screening of Rare Diseases
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
300
Start Date
2021-11-18
Completion Date
2027-06-01
Last Updated
2025-06-27
Healthy Volunteers
No
Conditions
Interventions
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.
Locations (1)
Cedars-Sinai Medical Centre (Los Angeles)
Los Angeles, California, United States