A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data
Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold th...
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| Format: | Article |
| Language: | English |
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Radcliffe Medical Media
2024-05-01
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| Series: | Arrhythmia & Electrophysiology Review |
| Online Access: | https://www.aerjournal.com/articleindex/aer.2023.25 |
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| author | Ovais A Jaffery Lea Melki Gregory Slabaugh Wilson W Good Caroline H Roney |
| author_facet | Ovais A Jaffery Lea Melki Gregory Slabaugh Wilson W Good Caroline H Roney |
| author_sort | Ovais A Jaffery |
| collection | DOAJ |
| description | Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data. |
| format | Article |
| id | doaj-art-2f504fabc521456a99bfd7b5920e8d5f |
| institution | OA Journals |
| issn | 2050-3369 2050-3377 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Radcliffe Medical Media |
| record_format | Article |
| series | Arrhythmia & Electrophysiology Review |
| spelling | doaj-art-2f504fabc521456a99bfd7b5920e8d5f2025-08-20T01:56:38ZengRadcliffe Medical MediaArrhythmia & Electrophysiology Review2050-33692050-33772024-05-011310.15420/aer.2023.25A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping DataOvais A Jaffery0Lea Melki1Gregory Slabaugh2Wilson W Good3Caroline H Roney4School of Engineering and Materials Science, Queen Mary University of London, London, UKR&D Algorithms, Acutus Medical, Carlsbad, CA, USDigital Environment Research Institute, Queen Mary University of London, London, UKR&D Algorithms, Acutus Medical, Carlsbad, CA, USSchool of Engineering and Materials Science, Queen Mary University of London, London, UKComputational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.https://www.aerjournal.com/articleindex/aer.2023.25 |
| spellingShingle | Ovais A Jaffery Lea Melki Gregory Slabaugh Wilson W Good Caroline H Roney A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data Arrhythmia & Electrophysiology Review |
| title | A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data |
| title_full | A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data |
| title_fullStr | A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data |
| title_full_unstemmed | A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data |
| title_short | A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data |
| title_sort | review of personalised cardiac computational modelling using electroanatomical mapping data |
| url | https://www.aerjournal.com/articleindex/aer.2023.25 |
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