SCH: Simulation Optimization of Cardiac Surgical Planning

SCH：心脏手术计划的模拟优化

• 批准号: 10816654
• 负责人: Hui Yang
• 金额: $ 29.7万
• 依托单位: UNIVERSITY OF TENNESSEE KNOXVILLE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-07 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816654
• 关键词: Ablation; Algorithms; Arrhythmia; Artificial Intelligence; Atrial Fibrillation; Automobile Driving; Cardiac; Cardiac Surgery procedures; Cardiac ablation; Cardiology; Catheters; Clinical; Computer Models; Computer Simulation; Coupled; Data; Decision Making; Developing Countries; Diagnosis; Disease; Effectiveness; Elderly; Electrophysiology (science); Environment; Evaluation; Family; Formulation; Foundations; Future; Goals; Health; Health Professional; Healthcare Systems; Heart; Heart Diseases; Human; Institutional Review Boards; Investments; Knowledge; Learning; Location; Machine Learning; Maps; Medical; Medical Students; Methodology; Mission; Modeling; Modernization; Monitor; National Heart, Lung, and Blood Institute; Operative Surgical Procedures; Pathway interactions; Patients; Performance; Personal Satisfaction; Physics; Physiological; Policies; Population; Postoperative Period; Procedures; Process; Protocols documentation; Publishing; Research; Signal Transduction; Societies; Solid; Source; Surgical incisions; Technology; Time; Training; Translating; Trees; Uncertainty; Variant; base; clinical application; clinical practice; coping; data streams; data-driven model; deep learning; design; experimental study; fighting; frontier; graph neural network; health care disparity; improved; information processing; innovation; insight; knowledge base; models and simulation; multiple data sources; network models; novel; novel strategies; open source; operation; response; rural area; sensor; simulation; skills; success; surgery outcome; tool; transfer learning; virtual

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.

许多患者采取手术干预措施与心脏病作斗争。手术成功是 对患者的健康及其家庭健康至关重要。对于例如，心房颤动（AF）是最大的 老年人口的普通心律失常。导管消融是对AF的既定治疗方法， 依次创建切口线以阻断故障的电路。但是，有很大的变化 在手术结果中。现代医疗保健系统在感知和计算方面投入了大量投资 提高信息可见性并应对疾病复杂性的技术。大量数据很容易 在手术环境中可用。实现最佳决策支持的完整数据潜力 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新的传感器的新型心脏病学领域 建模和仿真优化方法。该项目的目的是优化AF 通过将启用仿真计划与物理学的机器学习的传感器进行整合来消融 接受AF消融的患者的信号。这个目标将通过追求3来实现 具体目的：1）用于心脏建模的物理学的人工智能（AI） - 这种方法将 吸收异质感应数据，并将电生理学的先验知识纳入深度 学会增加不确定性下决策的鲁棒性，从而驾驶计算机 模拟临床应用； 2）最佳感应和时空AF的顺序学习 动力学 - 这种方法将提供疾病机制的定量知识，而不是 很难翻译（或转移）的主观知识，从而降低了由于 在粗糙地区的人类专家的可用性； 3）集成基于传感器的学习和模拟 针对外科计划的优化 - 这种方法将整合物理增强的建模（AIM 1）和 基于传感器的学习（AIM 2）具有模拟优化，以改善临床实践 数据驱动和模拟引导的手术计划。这个项目将为 Precision心脏病学由（i）超出了主要基于专家或临时决定的当前实践， （ii）捕获时空心脏动力学中的潜在复杂性，以及（iii）整合基于物理的 建模，基于传感器的学习和基于模拟的手术决策支持计划。