Mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapy
The review is devoted to the current status of artificial intelligence (AI), mathematical modeling (MM) and their combination in the analysis of cardiac dyssynchrony mechanisms and its treatment using cardiac resynchronization therapy (CRT). Recent articles and reviews demonstrate the high promise o...
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| Format: | Article |
| Language: | Russian |
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«FIRMA «SILICEA» LLC
2025-02-01
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| Series: | Российский кардиологический журнал |
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| Online Access: | https://russjcardiol.elpub.ru/jour/article/view/6194 |
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| author | T. M. Nesterova V. Yu. Kabak D. S. Lebedev O. E. Solovyova |
| author_facet | T. M. Nesterova V. Yu. Kabak D. S. Lebedev O. E. Solovyova |
| author_sort | T. M. Nesterova |
| collection | DOAJ |
| description | The review is devoted to the current status of artificial intelligence (AI), mathematical modeling (MM) and their combination in the analysis of cardiac dyssynchrony mechanisms and its treatment using cardiac resynchronization therapy (CRT). Recent articles and reviews demonstrate the high promise of AI and MM in personalized medicine, but also identify existing obstacles to their implementation in clinical practice. The works discussed are devoted to a number of topical problems of clinical cardiology as follows: analysis of phenotypes of patients with cardiac dyssynchrony, search for novel prognostic factors of CRT effectiveness, pacing optimization, creation of highly accurate predictive models of response to CRT. For the first time, a review of studies is given that use combined approaches of mechanistic MM and AI. Such approaches break new ground for the application of personalized heart models both for generating realistic synthetic data (digital twins) on which AI models are trained, and as predictors that, along with clinical signs, are used in trained prognostic AI models to improve the accuracy of personalized diagnostics, predict the effectiveness and optimize treatment. The review consists of three sections focused on studies using AI, personalized MM of heart ventricles, and combined approaches (MM+AI). |
| format | Article |
| id | doaj-art-0ef1499f69b54c4cb0cad05112d214a7 |
| institution | Kabale University |
| issn | 1560-4071 2618-7620 |
| language | Russian |
| publishDate | 2025-02-01 |
| publisher | «FIRMA «SILICEA» LLC |
| record_format | Article |
| series | Российский кардиологический журнал |
| spelling | doaj-art-0ef1499f69b54c4cb0cad05112d214a72025-08-20T03:43:45Zrus«FIRMA «SILICEA» LLCРоссийский кардиологический журнал1560-40712618-76202025-02-01294S10.15829/1560-4071-2024-61944297Mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapyT. M. Nesterova0V. Yu. Kabak1D. S. Lebedev2O. E. Solovyova3Institute of Immunology and PhysiologyInstitute of Immunology and Physiology; Ural Federal UniversityInstitute of Immunology and Physiology; Almazov National Medical Research CenterInstitute of Immunology and Physiology; Ural Federal UniversityThe review is devoted to the current status of artificial intelligence (AI), mathematical modeling (MM) and their combination in the analysis of cardiac dyssynchrony mechanisms and its treatment using cardiac resynchronization therapy (CRT). Recent articles and reviews demonstrate the high promise of AI and MM in personalized medicine, but also identify existing obstacles to their implementation in clinical practice. The works discussed are devoted to a number of topical problems of clinical cardiology as follows: analysis of phenotypes of patients with cardiac dyssynchrony, search for novel prognostic factors of CRT effectiveness, pacing optimization, creation of highly accurate predictive models of response to CRT. For the first time, a review of studies is given that use combined approaches of mechanistic MM and AI. Such approaches break new ground for the application of personalized heart models both for generating realistic synthetic data (digital twins) on which AI models are trained, and as predictors that, along with clinical signs, are used in trained prognostic AI models to improve the accuracy of personalized diagnostics, predict the effectiveness and optimize treatment. The review consists of three sections focused on studies using AI, personalized MM of heart ventricles, and combined approaches (MM+AI).https://russjcardiol.elpub.ru/jour/article/view/6194cardiac resynchronization therapyheart failurecardiac electrical and mechanical dyssynchronyartificial intelligence and machine learning in cardiologyheart mathematical modelspersonalized heart models in cardiology |
| spellingShingle | T. M. Nesterova V. Yu. Kabak D. S. Lebedev O. E. Solovyova Mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapy Российский кардиологический журнал cardiac resynchronization therapy heart failure cardiac electrical and mechanical dyssynchrony artificial intelligence and machine learning in cardiology heart mathematical models personalized heart models in cardiology |
| title | Mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapy |
| title_full | Mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapy |
| title_fullStr | Mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapy |
| title_full_unstemmed | Mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapy |
| title_short | Mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapy |
| title_sort | mathematical models and artificial intelligence for studying the ventricular dyssynchrony and improving the effectiveness of cardiac resynchronization therapy |
| topic | cardiac resynchronization therapy heart failure cardiac electrical and mechanical dyssynchrony artificial intelligence and machine learning in cardiology heart mathematical models personalized heart models in cardiology |
| url | https://russjcardiol.elpub.ru/jour/article/view/6194 |
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