دورية أكاديمية
أعرض في EDS

Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications.

التفاصيل البيبلوغرافية
العنوان: Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications.
المؤلفون: Islam MS; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada., Kalmady SV; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada., Hindle A; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada., Sandhu R; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA., Sun W; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada., Sepehrvand N; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada., Greiner R; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada., Kaul P; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada. Electronic address: pkaul@ualberta.ca.
المصدر: The Canadian journal of cardiology [Can J Cardiol] 2024 Jul 09. Date of Electronic Publication: 2024 Jul 09.
Publication Model: Ahead of Print
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: Elsevier Country of Publication: England NLM ID: 8510280 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1916-7075 (Electronic) Linking ISSN: 0828282X NLM ISO Abbreviation: Can J Cardiol Subsets: MEDLINE
أسماء مطبوعة: Publication: Jan./Feb. 2011- : Oxford, UK : Elsevier
Original Publication: [Oakville, Ont. : Canadian Cardiology Publications Inc., c1984-
مستخلص: Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
(Copyright © 2024 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)
تواريخ الأحداث: Date Created: 20240711 Latest Revision: 20240808
رمز التحديث: 20240808
DOI: 10.1016/j.cjca.2024.07.003
PMID: 38992812
قاعدة البيانات: MEDLINE
الوصف
تدمد:1916-7075
DOI:10.1016/j.cjca.2024.07.003