Publications
Vector
Amyloidosis
Artificial Intelligence–Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis
To develop an artificial intelligence (AI)–based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG).Read more
Published in Mayo Clinic Proceedings
July 2, 2021
Low Ejection Fraction
Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial Read more
Published in Nature Medicine
May 6, 2021
Low Ejection Fraction
Validation Of An Artificial Intelligence Electrocardiogram Based Algorithm For The Detection Of Left Ventricular Systolic Dysfunction In Subjects With Chagas Disease
Chagas cardiomyopathy is a frequent and severe manifestation of Chagas disease (CD) and it is a leading cause of morbidity and death in South America. The dilated cardiomyopathy in CD is often discovered only when patients present with symptomatic heart failure. We developed an artificial Read more
Published in Journal of the Americal College of Cardiology (JACC) / ACC.21
May 1, 2021
AF in Normal Sinus Rhythm
Artificial Intelligence Helps Identify Patients With Graves' Disease At Risk For Atrial Fibrillation
Graves' disease (GD) is known to be associated with atrial fibrillation (AF). Artificial intelligence (AI)-enabled ECGs using a convolutional neural network can identify the signature of silent AF. Whether the existing AI model is able to identify patients at highest risk of GD-related AF is Read more
Published in Journal of the Americal College of Cardiology (JACC) / ACC.21
May 1, 2021
Aortic Stenosis
Understanding Spectrum Bias In Algorithms Derived By Artificial Intelligence A Case Study In Detecting Aortic Stenosis Using Electrocardiograms
There are an increasing number of diagnostic tests derived from artificial intelligence (AI) and machine learning algorithms. Spectrum bias can arise when a diagnostic test is derived from study populations with different disease spectra than the target population. This bias is well described Read more
Published in Journal of the Americal College of Cardiology (JACC) / ACC.21
May 1, 2021
Amyloidosis
Artificial-Intelligence Enhanced Screening For Cardiac Amyloidosis By Electrocardiography
Cardiac amyloidosis (CA) is a life-threatening disease with poor outcomes often related to delayed diagnosis. We developed an artificial-intelligence (AI) based screening tool that identifies CA from the 12 lead ECG as well as single and six lead acquisitions. We collected 12-lead ECG data for Read more
Published in Journal of the Americal College of Cardiology (JACC) / ACC.21
May 1, 2021
COVID
AI Enhanced ECG Enabled Rapid Non-invasive Exclusion Of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Infection
Rapid identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is critical to management of the pandemic. We sought to investigate the use of artificial intelligence applied to the ECG to rule out acute COVID-19. A global, volunteer consortium from 4 continents Read more
Published in Journal of the Americal College of Cardiology (JACC) / ACC.21
May 1, 2021
Hypertrophic Cardiomyopathy
Detection Of Hypertrophic Cardiomyopathy By Artificial Intelligence-Enabled Electrocardiography In Children And Adolescents
Hypertrophic cardiomyopathy (HCM) is a cause of morbidity and sudden cardiac death in children and adolescents. There is currently no established screening approach for HCM. We recently developed an artificial intelligence (AI) convolutional neural network (CNN) for the detection of HCM based on Read more
Published in Journal of the Americal College of Cardiology (JACC) / ACC.21
May 1, 2021
Low Ejection Fraction
External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction
The objective of this study was to validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to Read more
Published in International Journal of Cardiology
April 15, 2021
Aortic Stenosis
Electrocardiogram screening for aortic valve stenosis using artificial intelligence
Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional Read more
Published in European Heart Journal
March 22, 2021
AF in Normal Sinus Rhythm
Artificial Intelligence Enabled-Electrocardiography for the Detection of Cerebral Infarcts in Patients With Atrial Fibrillation
Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG Read more
Published in International Stroke Conference 2021
March 11, 2021
AF in Normal Sinus Rhythm
Artificial Intelligence–Electrocardiography to Predict Incident Atrial Fibrillation
An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its Read more
Published in Circulation: Arrhythmia and Electrophysiology
November 13, 2020
Low Ejection Fraction
Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients
An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram [TTE]) in cardiac intensive care unit (CICU) patients. We Read more
Published in International Journal of Cardiology
November 2, 2020
Low Ejection Fraction
Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19: A Case Series
COVID-19 can result in deterioration of cardiac function, which is associated with high mortality. A simple point-of-care diagnostic test to screen for ventricular dysfunction would be clinically useful to guide management. We sought to review the clinical experience with an artificial intelligence Read more
Published in Mayo clinic proceedings
November 1, 2020
Pulmonary Hypertension
An Automated Screening Algorithm Using Electrocardiograms for Pulmonary Hypertension
"Pulmonary hypertension (PH) is a life-threatening disease that is typically detected after significant pulmonary vascular remodeling has occurred. Longer diagnostic delays are associated with higher mortality and there is a need for a simple, fast, non-invasive PH screening tool. Currently, Read more
Published in American Journal of Respiratory and Critical Care Medicine / American Thoracic Society (ATS) 2021 International Conference
November 1, 2020
Low Ejection Fraction
Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients
An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit (CICU) patients, independent of the presence of LVSD Read more
Published in European Heart Journal - acute cardiovascular care
October 16, 2020
Low Ejection Fraction
Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea
Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this Read more
Published in Circulation: Arrhythmia and Electrophysiology
August 1, 2020
AF in Normal Sinus Rhythm
How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?
Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, Read more
Published in Circulation Research
June 1, 2020
Aortic Stenosis
Detection Of Aortic Stenosis Using An Artificial Intelligence-Enabled Electrocardiogram
Patients with moderate to severe aortic stenosis (AS) have increased mortality even when asymptomatic. We hypothesized, that artificial intelligence - (AI) enabled electrocardiogram (ECG) - an inexpensive, ubiquitous, 10 second test - could detect patients with moderate/severe AS. 263,570 Patients Read more
Published in Journal of the Americal College of Cardiology (JACC) / ACC.20
March 1, 2020
Low Ejection Fraction
Assessing and Mitigating Bias in Medical Artificial Intelligence - The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis
Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm Read more
Published in Circulation: Arrhythmia and Electrophysiology
February 16, 2020
Low Ejection Fraction
Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)
The article details the materials that will be used in a clinical trial - ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial [1]. It includes a clinician-facing action recommendation report that will translate an artificial Read more
Published in American Heart Journal
February 1, 2020
Hypertrophic Cardiomyopathy
Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead electrocardiography (ECG). In the combined datasets, mean age was 54.8 ± 15.9 years for the HCM Read more
Published in Journal of the Americal College of Cardiology (JACC)
February 1, 2020
AF in Normal Sinus Rhythm
Recurrent cryptogenic stroke: A potential role for an artificial intelligence-enabled electrocardiogram?
This is a case report that includes four of the patient’s standard 12-lead electrocardiograms (ECGs) recorded at different times: baseline ECG, first abnormal ECG identified by the artificial intelligence–enabled ECG (AI-ECG) algorithm, and ECGs performed at the time of the first and second Read more
Published in HeartRhythm Case Reports
January 9, 2020
Low Ejection Fraction
ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial
A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a Read more
Published in American Heart Journal
January 1, 2020
Low Ejection Fraction
Prospective Analysis of Utility of Signals From an Ecg-Enabled Stethoscope to Automatically Detect a Low Ejection Fraction Using Neural Network Techniques Trained From the Standard 12-Lead Ecg
ECG-enabled stethoscopes (ECG-steth) can acquire single lead ECGs during cardiac auscultation, and may facilitate real-time screening for pathologies not routinely identified during physical examination (eg, arrhythmias). The objective of this study was to demonstrate that an AI algorithm trained Read more
Published in Circulation
November 11, 2019
AF in Normal Sinus Rhythm
An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, Read more
Published in The Lancet
August 1, 2019
Low Ejection Fraction
Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction
Patients undergoing routine ECG may have undetected left ventricular (LV) dysfunction that warrants further echocardiographic assessment. However, identification of these patients can be challenging. We applied the algorithm to all ECGs interpreted by the Mayo Clinic ECG laboratory in September Read more
Published in Journal of Cardiovascular Electrophysiology
February 1, 2019
Low Ejection Fraction
Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram
Asymptomatic left ventricular dysfunction (ALVD) is present in 3–6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found. An inexpensive, noninvasive screening tool for ALVD in the doctor’s office is not available. We tested the hypothesis Read more
Published in Nature Medicine
January 1, 2019
Low Ejection Fraction
Application Of Artificial Intelligence To The Standard 12 Lead Ecg To Identify People With Left Ventricular Dysfunction
Asymptomatic left ventricular dysfunction (ALVD) is present in 2-9% of the population, is associated with reduced longevity and is treatable when found. Inexpensive, reliable, in office screening is not available. The area under the curve (AUC) for a BNP screening blood test is 0.79 to 0.89. We Read more
Published in Journal of the Americal College of Cardiology (JACC) / ACC.18
March 1, 2018

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