SCH: INT: A Virtual Surgery Simulator to Accelerate Medical Training in Cardiovascular Disease

SCH：INT：加速心血管疾病医疗培训的虚拟手术模拟器

• 批准号: 10412769
• 负责人: Alison L Marsden
• 金额: $ 31.49万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10412769
• 关键词: 3-Dimensional; Address; Adoption; Anatomic Models; Anatomy; Artificial Intelligence; Biomechanics; Blood Vessels; Blood flow; Cardiac; Cardiovascular Diseases; Cardiovascular Models; Cardiovascular system; Classification; Clinical; Clinical Research; Collaborations; Communities; Computer Graphics; Computer software; Data; Data Science; Data Set; Databases; Development; Disease; Education; Elements; Ensure; FAIR principles; Feedback; Fostering; Funding; Goals; Gold; Grant; Hand; High Performance Computing; Hour; Image; Industry; International; Intuition; Investments; Joints; Knowledge acquisition; Lead; Machine Learning; Medical; Medical Device Designs; Medical Imaging; Medical Students; Medicine; Methods; Modeling; Neck; Operative Surgical Procedures; Parents; Pathology; Patients; Performance; Physics; Physiology; Process; Recording of previous events; Research; Research Personnel; Resources; Risk Assessment; Running; Science; Source Code; Technology; Time; Training; Twin Multiple Birth; United States National Institutes of Health; United States National Library of Medicine; Visualization; base; cluster computing; cohort; computing resources; data format; data repository; data resource; data structure; deep learning; design; digital; educational atmosphere; experience; imaging Segmentation; imaging modality; improved; insight; interactive feedback; interest; learning network; learning strategy; meetings; models and simulation; novel; open data; open source; prevent; repository; science education; simulation; software development; symposium; three-dimensional modeling; time use; tool; treatment planning; virtual reality; virtual surgery

The objective of the parent proposal is to devise and deploy an integrated virtual surgery simulator to transform training and surgical planning in cardiovascular medicine. By advancing science in graphics, visualization and real-time simulations, and interfacing with virtual reality (VR) technology, we will offer clinical trainees insight into cardiac physiology and pathology, accelerating knowledge acquisition and intuition-building in ways not possible with current tools. Despite recent advances in cardiovascular patient-specific modeling and blood flow simulation, current virtual surgery capabilities are limited to cumbersome by-hand model manipulations and blood flow simulations run on high-performance computing clusters for days at a time. These complexities preclude hands- on use by clinicians and often limit the models to a small cohort of anatomic designs. To drive this technology, the parent proposal contained the following specific aims: 1) Develop computer-graphics tools for efficient model manipulation, 2) Integrate reduced-order modeling with visualization to create a real-time interactive experience, and 3) Develop and deploy an interactive VR educational environment for medical students and clinical trainees. In support of these efforts, in this supplement proposal we will ready the Vascular Model Repository (VMR) for use in artificial intelligence (AI) and machine / deep learning (ML/DL) applications. The VMR is an open database of medical image data, segmented vascular models, and blood flow simulation results developed with support from the National Library of Medicine. We will use the VMR to support two major AI/ML efforts in the community. First, we will greatly accelerate anatomic model construction by DL for image segmentation, overcoming a major bottle neck to studies with large cohorts of patients. Second, we will develop physics-informed ML methods to drastically reduce current lengthy simulation times and provide fast and interactive feedback for surgical and interventional planning. This will produce fast and deployable ``digital twins'' that can provide interactive feedback to clinicians; these methods will be in direct support of the parent proposal and of general interest to the field. The supplement proposal contains three specific aims: 1) To ready the VMR for AI/ML by users who are not domain experts and spark interest in cardiovascular applications in the ML community, 2) To run community challenges in image segmentation and physics-informed ML at a major international meeting to identify best in class ML/DL methods, and 3) To demonstrate ML/DL methods in interactive surgical planning applications by integrating efforts with the parent proposal. We will disseminate our findings, methods, data, and source code to the research community via the open source SimVascular software project and the open data VMR. Our team brings together expertise in cardiovascular biomechanics and finite element modeling, computer graphics and reduced order modeling, physics informed ML, and development of open source and open data resources for the scientific community.

父母建议的目的是设计和部署集成的虚拟手术模拟器来转换 心血管医学的培训和外科计划。通过推进图形科学，可视化和 实时模拟，并与虚拟现实（VR）技术接口，我们将为临床学员提供洞察力 心脏生理和病理学，以不可能的方式加速知识获取和直觉建设 使用当前工具。尽管心血管特异性建模和血流模拟最近取得了进步，但 当前的虚拟手术能力仅限于繁琐的手机模型操纵和血流 模拟一次在高性能计算簇上进行几天。这些复杂性阻止了 在使用临床医生时，通常将模型限制为一小部分解剖设计。为了推动这项技术， 父母提案包含以下特定目的：1）开发计算机图纸工具以进行有效的模型 操纵，2）将减少订单建模与可视化集成以创建实时交互式体验， 3）为医学生和临床学员开发和部署互动的VR教育环境。 为了支持这些努力，在此补充提案中，我们将为血管模型存储库（VMR）准备 用于人工智能（AI）和机器 /深度学习（ML / DL）应用。 VMR是一个开放数据库 医疗图像数据，分段的血管模型以及支持的血流模拟结果 来自国家医学图书馆。我们将使用VMR支持社区中的两项主要AI/ML努力。 首先，我们将通过DL大大加速解剖模型来进行图像分割，克服一个主要 瓶颈进行大量患者研究。其次，我们将开发出具有物理信息的ML方法 大幅度减少当前的冗长模拟时间，并为手术和 介入计划。这将产生快速且可部署的``数字双胞胎''，可以提供交互式反馈 给临床医生；这些方法将直接支持父母建议和该领域的普遍关注。 补充提案包含三个具体目的：1）不属于AI/ML的VMR 领域专家并激发了对ML社区中心血管应用的兴趣，2）经营社区 在一次大型国际会议上，图像细分和物理知识的ML挑战，以确定最佳 ML/DL类方法和3）通过 将努力与父母建议融合。我们将把我们的发现，方法，数据和源代码传播到 通过开源的辛血管软件项目和开放数据VMR的研究社区。我们的团队 汇集了心血管生物力学和有限元建模，计算机图形和 减少订单建模，物理知识的ML以及开源和开放数据资源的开发 科学界。