Noseworthy, Peter A.

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., et al. (2019) · The Lancet

ECG Monitoring and AnalysisAtrial Fibrillation Management and Outcomes

Cited by 1,613

Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram

Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 1,389

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation

Joglar, J. A., Chung, M. K., Armbruster, A. L., Benjamin, E. J., Chyou, J. Y., Cronin, E. M., Deswal, A., Eckhardt, L. L., Goldberger, Z. D., Gopinathannair, R., et al. (2024) · Journal of the American College of Cardiology

Atrial Fibrillation Management and OutcomesAcute Ischemic Stroke Management

Cited by 933

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021) · Nature Reviews Cardiology

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 914

Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial

Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., et al. (2021) · Nature Medicine

Artificial Intelligence in Healthcare and EducationECG Monitoring and Analysis

Cited by 424

Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram

Ko, W.-Y., Siontis, K. C., Attia, Z. I., Carter, R. E., Kapa, S., Ommen, S. R., Demuth, S. J., Ackerman, M. J., Gersh, B. J., Arruda-Olson, A. M., et al. (2020) · Journal of the American College of Cardiology

ECG Monitoring and AnalysisCardiac electrophysiology and arrhythmias

Cited by 404

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., et al. (2019) · JAMA Cardiology

ECG Monitoring and AnalysisPotassium and Related Disorders

Cited by 318

Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial

Noseworthy, P. A., Attia, Z. I., Behnken, E. M., Giblon, R. E., Bews, K. A., Liu, S., Gosse, T. A., Linn, Z. D., Deng, Y., Yin, J., et al. (2022) · The Lancet

Atrial Fibrillation Management and OutcomesECG Monitoring and Analysis

Cited by 257

Meta-Analysis of Selective Serotonin Reuptake Inhibitor–Associated QTc Prolongation

Beach, S. R., Kostis, W. J., Celano, C. M., Januzzi, J. L., Ruskin, J. N., Noseworthy, P. A., & Huffman, J. C. (2014) · The Journal of Clinical Psychiatry

Cardiac electrophysiology and arrhythmiasCardiac Health and Mental Health

Cited by 253

Electrocardiogram screening for aortic valve stenosis using artificial intelligence

Cohen-Shelly, M., Attia, Z. I., Friedman, P. A., Ito, S., Essayagh, B. A., Ko, W.-Y., Murphree, D. H., Michelena, H. I., Enriquez-Sarano, M., Carter, R. E., et al. (2021) · European Heart Journal

Cardiac Valve Diseases and TreatmentsECG Monitoring and Analysis

Cited by 252

Prevalence of Transthyretin Amyloid Cardiomyopathy in Heart Failure With Preserved Ejection Fraction

AbouEzzeddine, O. F., Davies, D. R., Scott, C. G., Fayyaz, A. U., Askew, J. W., McKie, P. M., Noseworthy, P. A., Johnson, G. B., Dunlay, S. M., Borlaug, B. A., et al. (2021) · JAMA Cardiology

Amyloidosis: Diagnosis, Treatment, OutcomesHeart Failure Treatment and Management

Cited by 220

Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis

Noseworthy, P. A., Attia, Z. I., Brewer, L. C., Hayes, S. N., Yao, X., Kapa, S., Friedman, P. A., & Lopez-Jimenez, F. (2020) · Circulation: Arrhythmia and Electrophysiology

Artificial Intelligence in Healthcare and EducationECG Monitoring and Analysis

Cited by 220

Subclinical and Device-Detected Atrial Fibrillation: Pondering the Knowledge Gap: A Scientific Statement From the American Heart Association

Noseworthy, P. A., Kaufman, E. S., Chen, L. Y., Chung, M. K., Elkind, M. S. V., Joglar, J. A., Leal, M. A., McCabe, P. J., Pokorney, S. D., Yao, X., et al. (2019) · Circulation

Atrial Fibrillation Management and OutcomesCardiac pacing and defibrillation studies

Cited by 171

Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department with Dyspnea

Adedinsewo, D., Carter, R. E., Attia, Z., Johnson, P., Kashou, A. H., Dugan, J. L., Albus, M., Sheele, J. M., Bellolio, F., Friedman, P. A., et al. (2020) · Circulation: Arrhythmia and Electrophysiology

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 169

Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction

Attia, Z. I., Harmon, D. M., Dugan, J., Manka, L., Lopez-Jimenez, F., Lerman, A., Siontis, K. C., Noseworthy, P. A., Yao, X., Klavetter, E. W., et al. (2022) · Nature Medicine

ECG Monitoring and AnalysisNon-Invasive Vital Sign Monitoring

Cited by 154

Assessment of trends in statin therapy for secondary prevention of atherosclerotic cardiovascular disease in US adults from 2007 to 2016

Yao, X., Shah, N. D., Gersh, B. J., Lopez-Jimenez, F., & Noseworthy, P. A. (2020) · JAMA Network Open

Lipoproteins and Cardiovascular HealthMedication Adherence and Compliance

Cited by 117

Artificial Intelligence-Enabled ECG for Left Ventricular Diastolic Function and Filling Pressure

Lee, E., Ito, S., Miranda, W. R., Lopez-Jimenez, F., Kane, G. C., Asirvatham, S. J., Noseworthy, P. A., Friedman, P. A., Carter, R. E., Borlaug, B. A., et al. (2024) · npj Digital Medicine

ECG Monitoring and AnalysisCardiovascular Function and Risk Factors

Cited by 106

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial

Yao, X., McCoy, R. G., Friedman, P. A., Shah, N. D., Barry, B. A., Behnken, E. M., Inselman, J. W., Attia, Z. I., & Noseworthy, P. A. (2020) · American Heart Journal

Artificial Intelligence in Healthcare and EducationECG Monitoring and Analysis

Cited by 98

Ability of Terminal QRS Notching to Distinguish Benign from Malignant Electrocardiographic Forms of Early Repolarization

Merchant, F. M., Noseworthy, P. A., Weiner, R. B., Singh, S. M., Ruskin, J. N., & Reddy, V. Y. (2009) · The American Journal of Cardiology

Cardiac electrophysiology and arrhythmiasCardiac Arrhythmias and Treatments

Cited by 96

Artificial Intelligence-Augmented Electrocardiogram Detection of Left Ventricular Systolic Dysfunction in the General Population

Kashou, A. H., Medina-Inojosa, J. R., Noseworthy, P. A., Rodeheffer, R. J., Lopez-Jimenez, F., Attia, I. Z., Kapa, S., Scott, C. G., Lee, A. T., Friedman, P. A., et al. (2021) · Mayo Clinic Proceedings

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 66

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial

Adedinsewo, D. A., Morales-Lara, A. C., Afolabi, B. B., Kushimo, O. A., Mbakwem, A. C., Ibiyemi, K. F., Ogunmodede, J. A., Raji, H. O., Ringim, S. H., Habib, A. A., et al. (2024) · Nature Medicine

Cardiovascular Issues in PregnancyArtificial Intelligence in Healthcare and Education

Cited by 63

How will machine learning inform the clinical care of atrial fibrillation?

Siontis, K. C., Yao, X., Pirruccello, J. P., Philippakis, A. A., & Noseworthy, P. A. (2020) · Circulation Research

Atrial Fibrillation Management and OutcomesMachine Learning in Healthcare

Cited by 61

Artificial intelligence electrocardiogram as a novel screening tool to detect a newly abnormal left ventricular ejection fraction after anthracycline-based cancer therapy

Jacobs, J. E. J., Greason, G., Mangold, K. E., Wildiers, H., Willems, R., Janssens, S., Noseworthy, P., Lopez-Jimenez, F., Voigt, J.-U., Friedman, P., et al. (2024) · European Journal of Preventive Cardiology

Chemotherapy-induced cardiotoxicity and mitigationECG Monitoring and Analysis

Cited by 49

Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled Digital Stethoscope

Attia, Z. I., Dugan, J., Rideout, A., Maidens, J. N., Venkatraman, S., Guo, L., Noseworthy, P. A., Pellikka, P. A., Pham, S. L., Kapa, S., et al. (2022) · European Heart Journal - Digital Health

ECG Monitoring and AnalysisPhonocardiography and Auscultation Techniques

Cited by 46