Artificial Intelligence in Ventricular Arrhythmias and Sudden Death
Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Radcliffe Medical Media
2023-05-01
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| Series: | Arrhythmia & Electrophysiology Review |
| Online Access: | https://www.aerjournal.com/articleindex/aer.2022.42 |
| _version_ | 1797199987603406848 |
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| author | Lauri Holmström Frank Zijun Zhang David Ouyang Damini Dey Piotr J Slomka Sumeet S Chugh |
| author_facet | Lauri Holmström Frank Zijun Zhang David Ouyang Damini Dey Piotr J Slomka Sumeet S Chugh |
| author_sort | Lauri Holmström |
| collection | DOAJ |
| description | Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field. |
| first_indexed | 2024-03-07T17:40:01Z |
| format | Article |
| id | doaj.art-70a0844cae41418682934464f7d0843b |
| institution | Directory Open Access Journal |
| issn | 2050-3369 2050-3377 |
| language | English |
| last_indexed | 2024-04-24T07:24:29Z |
| publishDate | 2023-05-01 |
| publisher | Radcliffe Medical Media |
| record_format | Article |
| series | Arrhythmia & Electrophysiology Review |
| spelling | doaj.art-70a0844cae41418682934464f7d0843b2024-04-20T16:03:28ZengRadcliffe Medical MediaArrhythmia & Electrophysiology Review2050-33692050-33772023-05-011210.15420/aer.2022.42Artificial Intelligence in Ventricular Arrhythmias and Sudden DeathLauri Holmström0Frank Zijun Zhang1David Ouyang2Damini Dey3Piotr J Slomka4Sumeet S Chugh5Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US; Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA, USDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, USDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, USDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, USDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, USDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US; Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA, USSudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field.https://www.aerjournal.com/articleindex/aer.2022.42 |
| spellingShingle | Lauri Holmström Frank Zijun Zhang David Ouyang Damini Dey Piotr J Slomka Sumeet S Chugh Artificial Intelligence in Ventricular Arrhythmias and Sudden Death Arrhythmia & Electrophysiology Review |
| title | Artificial Intelligence in Ventricular Arrhythmias and Sudden Death |
| title_full | Artificial Intelligence in Ventricular Arrhythmias and Sudden Death |
| title_fullStr | Artificial Intelligence in Ventricular Arrhythmias and Sudden Death |
| title_full_unstemmed | Artificial Intelligence in Ventricular Arrhythmias and Sudden Death |
| title_short | Artificial Intelligence in Ventricular Arrhythmias and Sudden Death |
| title_sort | artificial intelligence in ventricular arrhythmias and sudden death |
| url | https://www.aerjournal.com/articleindex/aer.2022.42 |
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