Mostrar el registro sencillo del ítem
dc.contributor.author | Martínez-Sellés, Manuel | |
dc.contributor.author | Marina-Breysse, Manuel | |
dc.date.accessioned | 2023-07-17T10:47:06Z | |
dc.date.available | 2023-07-17T10:47:06Z | |
dc.date.issued | 2023-04-17 | |
dc.identifier.citation | J Cardiovasc Dev Dis. 2023 Apr 17;10(4):175 | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/16269 | |
dc.description.abstract | Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed. | es_ES |
dc.description.sponsorship | Manuel Marina-Breysse has received funding from European Union’s Horizon 2020 research and innovation program under the grant agreement number 965286; Machine Learning and Artificial Intelligence for Early Detection of Stroke and Atrial Fibrillation, MAESTRIA Consortium; and EIT Health, a body of the European Union. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | es_ES |
dc.type.hasVersion | VoR | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Current and Future Use of Artificial Intelligence in Electrocardiography. | es_ES |
dc.type | review | es_ES |
dc.rights.license | Atribución 4.0 Internacional | * |
dc.identifier.pubmedID | 37103054 | es_ES |
dc.format.volume | 10 | es_ES |
dc.format.number | 4 | es_ES |
dc.identifier.doi | 10.3390/jcdd10040175 | es_ES |
dc.contributor.funder | Unión Europea. Comisión Europea. H2020 | es_ES |
dc.description.peerreviewed | Sí | es_ES |
dc.identifier.e-issn | 2308-3425 | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/jcdd10040175 | es_ES |
dc.identifier.journal | Journal of cardiovascular development and disease | es_ES |
dc.repisalud.orgCNIC | CNIC::Grupos de investigación::Desarrollo Avanzado sobre Mecanismos y Terapias de las Arritmias | es_ES |
dc.repisalud.institucion | CNIC | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/965286 | es_ES |
dc.rights.accessRights | open access | es_ES |