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The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy

Background: Cardiovascular disease remains the leading cause of death in the European Union and worldwide. Constant improvement in cardiac care is leading to an increased number of patients with heart failure, which is a challenging condition in terms of clinical management. Cardiac resynchronizatio...

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Autores principales: Krzowski, Bartosz, Rokicki, Jakub, Główczyńska, Renata, Fajkis-Zajączkowska, Nikola, Barczewska, Katarzyna, Mąsior, Mariusz, Grabowski, Marcin, Balsam, Paweł
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778735/
https://www.ncbi.nlm.nih.gov/pubmed/35050227
http://dx.doi.org/10.3390/jcdd9010017
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author Krzowski, Bartosz
Rokicki, Jakub
Główczyńska, Renata
Fajkis-Zajączkowska, Nikola
Barczewska, Katarzyna
Mąsior, Mariusz
Grabowski, Marcin
Balsam, Paweł
author_facet Krzowski, Bartosz
Rokicki, Jakub
Główczyńska, Renata
Fajkis-Zajączkowska, Nikola
Barczewska, Katarzyna
Mąsior, Mariusz
Grabowski, Marcin
Balsam, Paweł
author_sort Krzowski, Bartosz
collection PubMed
description Background: Cardiovascular disease remains the leading cause of death in the European Union and worldwide. Constant improvement in cardiac care is leading to an increased number of patients with heart failure, which is a challenging condition in terms of clinical management. Cardiac resynchronization therapy is becoming more popular because of its grounded position in guidelines and clinical practice. However, some patients do not respond to treatment as expected. One way of assessing cardiac resynchronization therapy is with ECG analysis. Artificial intelligence is increasing in terms of everyday usability due to the possibility of everyday workflow improvement and, as a result, shortens the time required for diagnosis. A special area of artificial intelligence is machine learning. AI algorithms learn on their own based on implemented data. The aim of this study was to evaluate using artificial intelligence algorithms for detecting inadequate resynchronization therapy. Methods: A total of 1241 ECG tracings were collected from 547 cardiac department patients. All ECG signals were analyzed by three independent cardiologists. Every signal event (QRS-complex) and rhythm was manually classified by the medical team and fully reviewed by additional cardiologists. The results were divided into two parts: 80% of the results were used to train the algorithm, and 20% were used for the test (Cardiomatics, Cracow, Poland). Results: The required level of detection sensitivity of effective cardiac resynchronization therapy stimulation was achieved: 99.2% with a precision of 92.4%. Conclusions: Artificial intelligence algorithms can be a useful tool in assessing the effectiveness of resynchronization therapy.
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spelling pubmed-87787352022-01-22 The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy Krzowski, Bartosz Rokicki, Jakub Główczyńska, Renata Fajkis-Zajączkowska, Nikola Barczewska, Katarzyna Mąsior, Mariusz Grabowski, Marcin Balsam, Paweł J Cardiovasc Dev Dis Article Background: Cardiovascular disease remains the leading cause of death in the European Union and worldwide. Constant improvement in cardiac care is leading to an increased number of patients with heart failure, which is a challenging condition in terms of clinical management. Cardiac resynchronization therapy is becoming more popular because of its grounded position in guidelines and clinical practice. However, some patients do not respond to treatment as expected. One way of assessing cardiac resynchronization therapy is with ECG analysis. Artificial intelligence is increasing in terms of everyday usability due to the possibility of everyday workflow improvement and, as a result, shortens the time required for diagnosis. A special area of artificial intelligence is machine learning. AI algorithms learn on their own based on implemented data. The aim of this study was to evaluate using artificial intelligence algorithms for detecting inadequate resynchronization therapy. Methods: A total of 1241 ECG tracings were collected from 547 cardiac department patients. All ECG signals were analyzed by three independent cardiologists. Every signal event (QRS-complex) and rhythm was manually classified by the medical team and fully reviewed by additional cardiologists. The results were divided into two parts: 80% of the results were used to train the algorithm, and 20% were used for the test (Cardiomatics, Cracow, Poland). Results: The required level of detection sensitivity of effective cardiac resynchronization therapy stimulation was achieved: 99.2% with a precision of 92.4%. Conclusions: Artificial intelligence algorithms can be a useful tool in assessing the effectiveness of resynchronization therapy. MDPI 2022-01-10 /pmc/articles/PMC8778735/ /pubmed/35050227 http://dx.doi.org/10.3390/jcdd9010017 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Krzowski, Bartosz
Rokicki, Jakub
Główczyńska, Renata
Fajkis-Zajączkowska, Nikola
Barczewska, Katarzyna
Mąsior, Mariusz
Grabowski, Marcin
Balsam, Paweł
The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy
title The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy
title_full The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy
title_fullStr The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy
title_full_unstemmed The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy
title_short The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy
title_sort use of machine learning algorithms in the evaluation of the effectiveness of resynchronization therapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778735/
https://www.ncbi.nlm.nih.gov/pubmed/35050227
http://dx.doi.org/10.3390/jcdd9010017
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