International Validation of Echocardiographic Artificial Intelligence Amyloid Detection Algorithm

Grant Duffy; Evangelos K Oikonomou; Nicholas Easton; Hiroki Usuku; Jigesh Patel; Yoshinori Katsumata; Daisuke Yamasawa; Lily Stern; Shinichi Goto; Kenichi Tsujita; Paul Cheng; Rohan Khera; Faraz S Ahmad; David Ouyang

doi:10.1016/j.jacadv.2025.102067

International Validation of Echocardiographic Artificial Intelligence Amyloid Detection Algorithm

JACC Adv. 2025 Aug 25:102067. doi: 10.1016/j.jacadv.2025.102067. Online ahead of print.

Authors

Affiliations

¹ Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
² Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, Connecticut, USA.
³ Department of Medicine, Northwestern Medicine, Chicago, Illinois, USA.
⁴ Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University, Chuo-ku, Kumamoto, Japan.
⁵ Department of Cardiology, Keio University School of Medicine, Shinjuku, Tokyo, Japan.
⁶ Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kumamoto University, Chuo-ku, Kumamoto, Japan; Department of Cardiology, Keio University School of Medicine, Shinjuku, Tokyo, Japan; Department of Medicine, Tokai University School of Medicine, Isehara, Kanagawa, Japan; One Brave Idea, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
⁷ Division of Cardiology, Department of Medicine, Stanford University, Palo Alto, California, USA.
⁸ Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA. Electronic address: david.ouyang@cshs.org.

PMID: 40965401
DOI: 10.1016/j.jacadv.2025.102067

Abstract

Background: Diagnosis of cardiac amyloidosis (CA) is often missed or delayed due to confusion with other causes of increased left ventricular wall thickness. Conventional transthoracic echocardiographic measurements like global longitudinal strain have shown promise in distinguishing CA, but with limited specificity.

Objectives: We conducted a multisite retrospective case-control study to investigate the performance of a computer vision algorithm for CA identification across multiple international sites.

Methods: EchoNet-left ventricular hypertrophy (LVH) is a computer vision deep learning algorithm for the detection of CA based on parasternal long axis and apical-4-chamber view videos. We evaluated EchoNet-LVH's ability to distinguish between the echocardiogram studies of 574 CA patients and 979 controls. We reported discrimination performance with an area under the receiver operating characteristic curve and associated sensitivity, specificity, and positive predictive value at the prespecified threshold.

Results: EchoNet-LVH had an area under the receiver operating characteristic curve of 0.896 (95% CI: 0.875-0.916). At the prespecified model threshold optimizing for specificity, EchoNet-LVH had a sensitivity of 0.644 (95% CI: 0.601-0.685), specificity of 0.988 (95% CI: 0.978-0.994), positive predictive value of 0.968 (95% CI: 0.944-0.984), and negative predictive value of 0.828 (95% CI: 0.804-0.850). There was no evidence of heterogeneity in performance by site, race, sex, age, body mass index, CA subtype, or ultrasound manufacturer.

Conclusion: EchoNet-LVH can assist with earlier and accurate diagnosis of CA. EchoNet-LVH achieved development goals to be highly specific to maximize positive predictive value of downstream confirmatory testing since CA is a rare disease.

Keywords: artificial intelligence; cardiac amyloidosis; computer vision; echocardiography.