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.
Patients who have a high suspicion for cardiac amyloidosis by AI algorithm
Patients who decline to be seen at specialty clinic
Patients who have passed away
Patients who have a high suspicion for cardiac amyloidosis by AI algorithm
Cohort
Prospective
EchoNet-LVH screening for cardiac amyloidosis
Other interventionAn 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.
Number of New Diagnoses of Cardiac Amyloidosis Found
From chart review, identification of patients who have a downstream diagnosis of cardiac amyloidosis
Number of New Diagnoses of TTR Amyloidosis Found
From chart review, identification of patients who have a downstream diagnosis of TTR amyloidosis
Number of New Diagnoses of AL Amyloidosis Found
From chart review, identification of patients who have a downstream diagnosis of AL amyloidosis
Click on the lead sponsor to view all of their trials.
