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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Journal of Cardiovascular Development and Disease |
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| Online Access: | https://www.mdpi.com/2308-3425/9/1/17 |
| _version_ | 1797493103177760768 |
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| author | Bartosz Krzowski Jakub Rokicki Renata Główczyńska Nikola Fajkis-Zajączkowska Katarzyna Barczewska Mariusz Mąsior Marcin Grabowski Paweł Balsam |
| author_facet | Bartosz Krzowski Jakub Rokicki Renata Główczyńska Nikola Fajkis-Zajączkowska Katarzyna Barczewska Mariusz Mąsior Marcin Grabowski Paweł Balsam |
| author_sort | Bartosz Krzowski |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T01:15:12Z |
| format | Article |
| id | doaj.art-6b383e387bfe4c41bcbd1b94db332089 |
| institution | Directory Open Access Journal |
| issn | 2308-3425 |
| language | English |
| last_indexed | 2024-03-10T01:15:12Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Cardiovascular Development and Disease |
| spelling | doaj.art-6b383e387bfe4c41bcbd1b94db3320892023-11-23T14:11:09ZengMDPI AGJournal of Cardiovascular Development and Disease2308-34252022-01-01911710.3390/jcdd9010017The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization TherapyBartosz Krzowski0Jakub Rokicki1Renata Główczyńska2Nikola Fajkis-Zajączkowska3Katarzyna Barczewska4Mariusz Mąsior5Marcin Grabowski6Paweł Balsam71st Department of Cardiology, Medical University of Warsaw, 02-097 Warsaw, Poland1st Department of Cardiology, Medical University of Warsaw, 02-097 Warsaw, Poland1st Department of Cardiology, Medical University of Warsaw, 02-097 Warsaw, PolandCardiomatics, 31-339 Cracow, PolandCardiomatics, 31-339 Cracow, PolandCardiomatics, 31-339 Cracow, Poland1st Department of Cardiology, Medical University of Warsaw, 02-097 Warsaw, Poland1st Department of Cardiology, Medical University of Warsaw, 02-097 Warsaw, PolandBackground: 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.https://www.mdpi.com/2308-3425/9/1/17artificial intelligenceheart failurecardiac resynchronization therapy |
| spellingShingle | Bartosz Krzowski Jakub Rokicki Renata Główczyńska Nikola Fajkis-Zajączkowska Katarzyna Barczewska Mariusz Mąsior Marcin Grabowski Paweł Balsam The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy Journal of Cardiovascular Development and Disease artificial intelligence heart failure cardiac 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 | artificial intelligence heart failure cardiac resynchronization therapy |
| url | https://www.mdpi.com/2308-3425/9/1/17 |
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