DOI: 10.1161/circ.148.suppl_1.17060 ISSN: 0009-7322

Abstract 17060: Preliminary Assessment of Artificial Intelligence Guided Echocardiographic Screening of Cardiac Amyloidosis

Lily K Stern, Irene R Marker, Grant Duffy, SANDY JOUNG, Alan C Kwan, Joseph E Ebinger, Florian Rader, Susan Cheng, Jignesh K Patel, David Ouyang
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Background: Cardiac amyloidosis (CA) diagnosis is often overlooked. Novel CA treatments underscore the importance of early identification, as untreated CA has a poor prognosis. EchoNet-Screening is an echocardiogram-based artificial intelligence model to identify CA; previously refined by echocardiograms from patients with known CA. The goal of our study is to prospectively evaluate the accuracy of EchoNet-Screening across a large health system.

Methods: All echocardiographic studies from our cardiac quaternary care center between 2019-2021 were batch-processed by EchoNet-Screening to produce a CA risk-prediction score (CA-RPS) for each patient based on the highest quality study, which is determined by the model. 336 patients with the highest CA-RPS underwent evaluation by detailed chart review by our CA clinical cardiology team. High risk patients without an alternative diagnosis are subsequently referred for CA specialty evaluation. (

Results: EchoNet-Screening was performed on echocardiographic images from 58,236 patients. Preliminary retrospective assessment of 336 patients with the highest CA-RPS revealed CA in 142 patients; almost half were not known from our advanced heart failure (AHF) registry and newly identified from chart review. 62 patients are pending prospective enrollment for CA subspecialty evaluation to further characterize the performance of EchoNet-Screening.

Conclusions: Preliminary results from our trial demonstrate a high accuracy of EchoNet-Screening for detection of patients with known CA, emphasized by high rates of identification of CA patients even on initial chart review. Further work is ongoing to evaluate the performance of EchoNet-Screening to bring high-risk patients to new diagnoses and eventual care.

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