Magoon Matthew J, Nazer Babak, Akoum Nazem, Boyle Patrick M
Department of Bioengineering, University of Washington, Seattle, WA, USA.
Division of Cardiology, University of Washington Medicine, Seattle, WA, USA.
Curr Cardiol Rep. 2024 Dec;26(12):1393-1403. doi: 10.1007/s11886-024-02136-0. Epub 2024 Sep 20.
Technology drives the field of cardiac electrophysiology. Recent computational advances will bring exciting changes. To stay ahead of the curve, we recommend electrophysiologists develop a robust appreciation for novel computational techniques, including deterministic, statistical, and hybrid models.
In clinical applications, deterministic models use biophysically detailed simulations to offer patient-specific insights. Statistical techniques like machine learning and artificial intelligence recognize patterns in data. Emerging clinical tools are exploring avenues to combine all the above methodologies. We review three ways that computational medicine will aid electrophysiologists by: (1) improving personalized risk assessments, (2) weighing treatment options, and (3) guiding ablation procedures. Leveraging clinical data that are often readily available, computational models will offer valuable insights to improve arrhythmia patient care. As emerging tools promote personalized medicine, physicians must continue to critically evaluate technology-driven tools they consider using to ensure their appropriate implementation.
技术推动心脏电生理学领域发展。近期的计算进展将带来令人兴奋的变革。为了紧跟潮流，我们建议电生理学家对新颖的计算技术，包括确定性模型、统计模型和混合模型，有深入的了解。
在临床应用中，确定性模型使用生物物理细节模拟来提供针对个体患者的见解。机器学习和人工智能等统计技术可识别数据中的模式。新兴的临床工具正在探索整合上述所有方法的途径。我们回顾计算医学将通过以下三种方式帮助电生理学家：（1）改进个性化风险评估，（2）权衡治疗方案，以及（3）指导消融手术。利用通常容易获得的临床数据，计算模型将提供有价值的见解，以改善心律失常患者的护理。随着新兴工具推动个性化医疗，医生必须继续审慎评估他们考虑使用的技术驱动工具，以确保其正确实施。
