Publications
Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction
Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested. In… Read More
Published in Nature Medicine
November 14, 2022
Community-based participatory research application of an artificial intelligence-enhanced electrocardiogram for cardiovascular disease screening: A FAITH! Trial ancillary study
We conducted this study to evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of participants enrolled in a community-based clinical trial as a proof-of-con… Read More
Published in American Journal of Preventive Cardiology
November 13, 2022
Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.
In this study, we aimed to compare the clinicians’ characteristics of “high adopters” and “low adopters” of an artificial intelligence (AI)–enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting pa… Read More
Published in Mayo Clinic Proceedings
November 1, 2022
Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice
An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM). However, its application in routine clinical practice may be challenging due to the low disease prevalence and potentially high false-positive rates. Thi… Read More
Published in Cardiovascular Digital Health Journal
October 22, 2022
Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial
We conducted a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognized atrial fibrillation. For this non-randomised interventional trial, we prospectively recruited patients with stroke risk… Read More
Published in The Lancet
September 27, 2022
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