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来对抗心脏病。手术成功率是 对病人的健康和他们的家庭幸福至关重要。例如，心房颤动（AF）是最常见的 老年人常见心律失常。导管消融术是房颤的一种既定治疗方法， 顺序地产生切口线以阻断故障的电通路。然而， 手术结果。现代医疗保健系统正在大力投资传感和计算 提高信息可见度和科普疾病复杂性的技术。海量数据很容易 在手术环境中可用。充分挖掘数据潜力，为最佳决策提供支持 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新的基于传感器的 建模和仿真优化方法。本项目的目标是优化AF 通过将模拟使能的规划与传感器的物理增强机器学习相结合来进行消融 接受房颤消融术患者的信号。这一目标将通过实现3 具体目标：1）用于心脏建模的物理增强人工智能（AI）-这种方法将 吸收异质感测数据并将电生理学先验知识结合到深度 学习在不确定性下增加决策的鲁棒性，从而驱动计算机 模拟临床应用; 2）时空AF的最佳感知和顺序学习 动态-这种方法将提供疾病机制的定量知识，而不是 难以翻译（或转移）的主观知识，从而减少由于 农村地区的人类专家的可用性; 3）整合基于传感器的学习和模拟 优化手术计划-该方法将整合物理增强建模（目标1）， 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手术计划。该项目将取得重大突破， 精确的心脏病学通过（i）超越目前主要基于专家或临时决定的实践， (ii)捕捉时空心脏动力学的潜在复杂性，以及（iii）整合基于物理学的 建模、基于传感器的学习和基于模拟的手术决策支持计划。