Jaffery Ovais A, Melki Lea, Slabaugh Gregory, Good Wilson W, Roney Caroline H
School of Engineering and Materials Science, Queen Mary University of London London, UK.
R&D Algorithms, Acutus Medical Carlsbad, CA, US.
Arrhythm Electrophysiol Rev. 2024 May 20;13:e08. doi: 10.15420/aer.2023.25. eCollection 2024.
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 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.
在过去几十年中，心脏电生理计算模型逐渐成熟，目前正朝着个性化方向发展，以提供针对患者的治疗指导，改善不理想的治疗效果。这些个性化电生理模型的预测特性有望实现最佳治疗方案规划，而目前临床中由于依赖基于群体或平均患者的方法，这方面受到限制。生成个性化电生理模型需要一系列步骤，针对这些步骤人们提出了一系列激活映射、校准方法和治疗模拟流程。然而，可能构成临床相关治疗的最佳方法仍在研究中，并且面临一些限制，如电解剖数据记录的不确定性、在临床时间范围内生成和校准模型以及验证或对标恢复的组织参数的要求。本文旨在报告心脏计算模型个性化技术，重点是基于电解剖映射数据校准心脏组织电导率。
