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dc.contributor.authorMuzammil, Muhammad Ali
dc.contributor.authorJavid, Saman
dc.contributor.authorAfridi, Azra Khan
dc.contributor.authorSiddineni, Rupini
dc.contributor.authorShahabi, Mariam
dc.contributor.authorHaseeb, Muhammad
dc.contributor.authorFariha, F.N.U.
dc.contributor.authorKumar, Satesh
dc.contributor.authorZaveri, Sahil
dc.contributor.authorNashwan, Abdulqadir J.
dc.date.accessioned2024-02-13T17:06:32Z
dc.date.available2024-02-13T17:06:32Z
dc.date.issued2024-03
dc.identifier.citationMuzammil MA, Javid S, Afridi AK, Siddineni R, Shahabi M, Haseeb M, Fariha FNU, Kumar S, Zaveri S, Nashwan AJ. Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. J Electrocardiol. 2024 Jan 28;83:30-40. doi: 10.1016/j.jelectrocard.2024.01.006. Epub ahead of print. PMID: 38301492.en_US
dc.identifier.issn0022-0736
dc.identifier.doi10.1016/j.jelectrocard.2024.01.006
dc.identifier.pmid38301492
dc.identifier.piiS0022073624000128
dc.identifier.urihttp://hdl.handle.net/20.500.12648/14673
dc.description.abstractElectrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.en_US
dc.description.sponsorshipQatar National Libraryen_US
dc.language.isoenen_US
dc.publisherElsevier BVen_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0022073624000128en_US
dc.rights© 2024 The Author(s). Published by Elsevier Inc.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttps://www.elsevier.com/tdm/userlicense/1.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCardiology and Cardiovascular Medicineen_US
dc.subjectArtificial intelligenceen_US
dc.subjectCardiovascular diseaseen_US
dc.subjectDiagnosisen_US
dc.subjectElectrocardiographyen_US
dc.titleArtificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseasesen_US
dc.typeArticle/Reviewen_US
dc.source.journaltitleJournal of Electrocardiologyen_US
dc.source.volume83
dc.source.beginpage30
dc.source.endpage40
dc.description.versionVoRen_US
refterms.dateFOA2024-02-13T17:06:34Z
dc.description.institutionSUNY Downstateen_US
dc.description.departmentMedicineen_US
dc.description.degreelevelN/Aen_US


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