Zobrazeno 1 - 10
of 553
pro vyhledávání: '"Francisco Lopez-jimenez"'
Autor:
Waldemar E. Wysokinski, MD, PhD, Ryan A. Meverden, PA-C, Francisco Lopez-Jimenez, MD, MBA, David M. Harmon, MD, Betsy J. Medina Inojosa, MD, Abraham Baez Suarez, PhD, MS, Kan Liu, PhD, Jose R. Medina Inojosa, MD, Ana I. Casanegra, MD, Robert D. McBane, MD, Damon E. Houghton, MD, MS
Publikováno v:
Mayo Clinic Proceedings: Digital Health, Vol 2, Iss 3, Pp 453-462 (2024)
Objective: To develop an artificial intelligence deep neural network (AI-DNN) algorithm to analyze 12-lead electrocardiogram (ECG) for detection of acute pulmonary embolism (PE) and PE categories. Patients and Methods: A cohort of patients seen betwe
Externí odkaz:
https://doaj.org/article/0e9d0dce6da349bfa08f668a7479b86e
Autor:
Peter Wohlfahrt, MD, PhD, Dominik Jenča, MD, Vojtěch Melenovský, MD, PhD, Jolana Mrázková, Mgr, Marek Šramko, MD, PhD, Martin Kotrč, MD, Michael Želízko, MD, Věra Adámková, MD, PhD, Francisco Lopez-Jimenez, MD, MSc, MBA, Jan Piťha, MD, PhD, Josef Kautzner, MD, PhD
Publikováno v:
Mayo Clinic Proceedings: Digital Health, Vol 2, Iss 3, Pp 352-360 (2024)
Objective: To evaluate the effect of smart device-based telerehabilitation on Vo2peak in patients after myocardial infarction. Patients and Methods: This was a pilot, single-center, randomized, cross-over study with a 3-month intervention. One month
Externí odkaz:
https://doaj.org/article/c54556d7a7594845a4ee28d4bb58a0e9
Autor:
Donnchadh O’Sullivan, Scott Anjewierden, Grace Greason, Itzhak Zachi Attia, Francisco Lopez-Jimenez, Paul A. Friedman, Peter Noseworthy, Jason Anderson, Anthony Kashou, Samuel J. Asirvatham, Benjamin W. Eidem, Jonathan N. Johnson, Talha Niaz, Malini Madhavan
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-5 (2024)
Abstract AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence
Externí odkaz:
https://doaj.org/article/68531a39abd54b56b82e3c0a5e7e6fc7
Autor:
Scott Anjewierden, Donnchadh O'Sullivan, Kathryn E. Mangold, Grace Greason, Itzhak Zachi Attia, Francisco Lopez‐Jimenez, Paul A. Friedman, Samuel J. Asirvatham, Jason Anderson, Benjamin W. Eidem, Jonathan N. Johnson, Shisheer Havangi Prakash, Talha Niaz, Malini Madhavan
Publikováno v:
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 13, Iss 21 (2024)
Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in a
Externí odkaz:
https://doaj.org/article/6e0449f78b5c4acdbb8a079e3d10ab46
Autor:
Jaskanwal D. S. Sara, Nazanin Rajai, Scott Breitinger, Betsy Medina‐Inojosa, Lilach O. Lerman, Francisco Lopez‐Jimenez, Amir Lerman
Publikováno v:
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 13, Iss 21 (2024)
Background A subset of individuals with major depressive disorder (MDD) have a high burden of cardiovascular risk factors and cerebral small‐vessel disease, implicating vascular disease in the development of depression. Cross‐sectional studies de
Externí odkaz:
https://doaj.org/article/b5e1261086c04837951a626eaf26a0d8
Autor:
Eunjung Lee, Saki Ito, William R. Miranda, Francisco Lopez-Jimenez, Garvan C. Kane, Samuel J. Asirvatham, Peter A. Noseworthy, Paul A. Friedman, Rickey E. Carter, Barry A. Borlaug, Zachi I. Attia, Jae K. Oh
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-7 (2024)
Abstract Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled el
Externí odkaz:
https://doaj.org/article/8a74fb9cbbf3475598191b5f7af8ade7
Autor:
Lee Herzog, MD, Ran Ilan Ber, PhD, Zehavi Horowitz-Kugler, MD, Yardena Rabi, BIMS, Ilan Brufman, BSc, Yehuda Edo Paz, MD, Francisco Lopez-Jimenez, MD, MSc, MBA
Publikováno v:
Mayo Clinic Proceedings: Digital Health, Vol 1, Iss 4, Pp 632-640 (2023)
Objective: To develop and validate a machine learning model that predicts the most successful antihypertensive treatment for an individual. Patients and Methods: The causal, deep neural network-based model was trained on data from 16,917 newly diagno
Externí odkaz:
https://doaj.org/article/ddb6e1f9219c407aa017393c3d969ade
Autor:
Jose R. Medina‐Inojosa, Miguel A. Gomez Ibarra, Betsy J. Medina‐Inojosa, Marta Supervia, Sarah Jenkins, Lynne Johnson, Nathalie P. Suarez, Amanda Bonikowske, Virend K. Somers, Francisco Lopez‐Jimenez
Publikováno v:
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 13, Iss 8 (2024)
Background Extended sedentary behavior is a risk factor for chronic disease and mortality, even among those who exercise regularly. Given the time constraints of incorporating physical activity into daily schedules, and the high likelihood of sitting
Externí odkaz:
https://doaj.org/article/76f30f77b2424ba096bcfdd12123c385
Publikováno v:
Mayo Clinic Proceedings: Digital Health, Vol 1, Iss 2, Pp 105-108 (2023)
Externí odkaz:
https://doaj.org/article/8fbf589a977d4c2687828af45da2c82d
Autor:
Robert D. McBane, Dennis H. Murphree, David Liedl, Francisco Lopez‐Jimenez, Itzhak Zachi Attia, Adelaide M. Arruda‐Olson, Christopher G. Scott, Naresh Prodduturi, Steve E. Nowakowski, Thom W. Rooke, Ana I. Casanegra, Waldemar E. Wysokinski, Damon E. Houghton, Haraldur Bjarnason, Paul W. Wennberg
Publikováno v:
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 13, Iss 3 (2024)
Background Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all‐cause death. Developing tools capable of identifying those patients with peripheral artery disease at grea
Externí odkaz:
https://doaj.org/article/f37b3dfd89a24f6487ed35029cdafda9