SCH: Simulation Optimization of Cardiac Surgical Planning

许多患者采取外科干预措施与心脏病作斗争。外科手术的成功是 对患者的健康和他们的家庭福祉至关重要。例如，房颤(AF)是最常见的 老年人群常见的心律失常。导管消融是一种公认的治疗房颤的方法， 按顺序创建切割线以阻断有故障的电气路径。然而，有很大的变化 在手术结果上。现代医疗系统在传感和计算方面投入巨资 提高信息可见性和应对疾病复杂性的技术。海量数据随手可得 可在外科环境中使用。实现最优决策支持的全部数据潜力 这取决于信息处理和计算建模方法的进步。 我们的长期目标是通过开发基于传感器的新技术来推进精确心脏病学的前沿 建模和仿真优化方法。该项目的目标是优化自动对焦 集成模拟使能规划和物理增强的传感器机器学习的烧蚀 来自房颤消融患者的信号。这一目标将通过追求3个目标来实现 具体目标：1)物理学-用于心脏建模的增强人工智能(AI)-该方法将 同化异质传感数据，将电生理学先验知识融入深度 学会在不确定情况下提高决策的稳健性，从而驱动计算机 仿真在临床应用中的应用2)空时房颤的最优感知和序贯学习 动力学-这种方法将提供疾病机制的量化知识，而不是 难以转化(或转移)的主观知识，从而减少因以下原因造成的医疗差距 农村地区人类专家的可用性；3)集成基于传感器的学习和模拟 优化手术计划-此方法将整合物理增强建模(目标1)和 基于传感器的学习(目标2)与模拟优化，以改善临床实践 数据驱动和模拟引导的外科手术规划。该项目将在以下方面取得重大突破 精确的心脏病学通过(I)超越目前主要基于专家或特别决定的做法， (Ii)捕捉时空心脏动力学的潜在复杂性，以及(Iii)整合以物理为基础的 手术决策支持的建模、基于传感器的学习和基于模拟的规划。

Many patients take surgical interventions to fight the battle against heart disease. Surgical successes are critical to the patients’ health and their family well-being. For e.g., atrial fibrillation (AF) is the most common arrhythmia in elder population. Catheter ablation is an established treatment for AF, which sequentially creates incision lines to block faulty electrical pathways. However, there are large variations in surgical outcomes. Modern healthcare systems are investing heavily in sensing and computing technology to increase information visibility and cope with disease complexity. Massive data are readily available in the surgical environment. Realizing the full data potential for optimal decision support depends on the advancement of information processing and computational modeling methodologies. Our long-term goal is to advance the frontier of precision cardiology by developing new sensor-based modeling and simulation optimization methodologies. The objective of this project is to optimize AF ablation by integrating simulation-enabled planning with physics-augmented machine learning of sensor signals from patients who underwent AF ablation. This objective will be accomplished by pursuing 3 specific aims: 1) Physics-augmented artificial intelligence (AI) for cardiac modeling – This approach will assimilate heterogeneous sensing data and incorporate electrophysiology prior knowledge into deep learning to increase the robustness of decision making under uncertainty, thereby driving computer simulation into clinical applications; 2) Optimal sensing and sequential learning of space-time AF dynamics – This approach will provide quantitative knowledge of disease mechanisms instead of subjective knowledge that is difficult to translate (or transfer), thereby reducing healthcare disparity due to the availability of human experts in rural areas; 3) Integrating sensor-based learning and simulation optimization for surgical planning - This approach will integrate physics-augmented modeling (Aim 1) and sensor-based learning (Aim 2) with simulation optimization to improve the clinical practice towards data-driven & simulation-guided surgical planning. This project will make a major breakthrough towards precision cardiology by (i) going beyond the current practice of largely expert-based or ad hoc decisions, (ii) capturing underlying complexities in space-time cardiac dynamics, and (iii) integrating physics-based modeling, sensor-based learning, and simulation-based planning for surgical decision support.