Development of AI/ML-ready shared repository for parametric multiphysics modeling datasets: standardization for predictive modeling of selective brain cooling after traumatic injury

摘要 通过快速和选择性地冷却受伤的脑组织，我们可以极大地减轻创伤的长期影响。 击中头部。作为美国国立卫生研究院资助的R21的一部分，我们正在开发一种可以很容易地插入普通 使用脑室外导管来增加降温以控制颅内压。在我们开发该设备的同时， 我们也意识到需要使用AI/ML算法来优化装置的设计和治疗计划。 不幸的是，所有商业上可用的运行多相数值模拟的软件都产生了 还没有准备好通过人工智能和机器学习(AI/ML)技术进行处理。虽然AI/ML 数据驱动的技术可能会给生物医学研究带来革命性的变化吗？大多数研究数据都不会 易于被AI/ML应用程序使用。特别是，人们普遍而迫切地需要使AI-ML 准备好由多物理数值模拟生成的大型参数数据集。 该补充项目旨在解决该问题，并为其他临床/基础项目创建框架模板 研究小组将使复杂预测多物理建模的AI/ML就绪数据增强 值得注意的是他们的优化和预测能力。这些模拟可以快速而准确地预测 复杂生物医学设备在幻影、临床前和临床环境中的行为。参数预测 多物理建模(PPMM)允许研究人员/临床医生/患者研究潜在变化的影响 在制造中，治疗参数、解剖特征和对治疗的生理反应 程序。这些敏感性研究产生了可以由AI/ML快速处理的大量数据集 优化临床程序的算法。作为最近授予的R21赠款的一部分，我们正在开发一种新的 一种可以快速、选择性地冷却创伤性脑损伤患者脑组织的装置。快速选择 通过将二次损伤降至最低，大脑降温可以显著改善患者的预后。 使用商用软件(COMSOL、Ansys、MatLab、CST等)的PPMM在 在设计阶段和治疗计划阶段。然而，大量的PPMM数据并不是 准备好进行AI/ML处理，因为每个4D数据库缺乏对原始参数集(即组织)的参考 性质、灌注率、损伤类型和位置(…)。因此，我们计划在拟议的补充方案中 研究，以解决这些具体目标：1)开发和传播AI/ML就绪的PPMM数据集2) 演示支持AI/ML的PPMM数据集在AI/ML应用程序中的可用性(优化治疗 规划)3)通过学生参与活动展示AI/ML就绪的PPMM数据集的可用性。 尽管这项研究将集中在大脑冷却PPMM上，但该方法将很容易扩展到其他 PPMM如癌症热消融、低温心脏手术的脑温监测和早期 检测侵袭性乳腺癌。拟议的研究将为充分发挥AI/ML的潜力铺平道路 与多物理模拟相结合的技术，以造福创伤性脑损伤患者。

ABSTRACT By rapidly and selectively cooling injured brain tissue, we can dramatically mitigate the long-term effect of trauma to the head. As part of the NIH-funded R21, we are developing a stylet that could be easily inserted in commonly used extra ventricular catheters to add cooling to intracranial pressure control. As we are developing the device, we also realize the need for using AI/ML algorithm for optimizing design of the device and treatment planning. Unfortunately all the commercially available software that run multiphisic numerical simulation produce data that is not ready for processing by artificial intelligence and machine learning (AI/ML) technologies. Although AI/ML are data-driven technologies could potentially revolutionize biomedical research, most research data is not readily useable by AI/ML applications. In particular, there is the widespread and urgent need to make AI-ML ready the large parametric datasets generated by multiphysics numerical simulations. This supplemental project aim to address that issue and create a framework template for other clinical/basic research groups to make AI/ML ready data from complex predictive multiphysics modeling to enhance significantly their optimization and prediction capabilities. These simulations can rapidly and accurately predict the behavior of complex biomedical devices in phantom, preclinical and clinical settings. Parametric predictive multiphysics modeling (PPMM) allows researchers/clinicians/patients to study the effects of potential variations in manufacturing, treatment parameters, anatomical features and physiological responses on treatment procedures. These sensitivity studies produce significantly large datasets that could be rapidly process by AI/ML algorithms to optimize clinical procedures. As part of a recently awarded R21 grant, we are developing a new device that can rapidly and selectively cool the cerebral tissue of traumatic brain injury patients. Rapid selective brain cooling could dramatically improve patient outcomes by minimizing secondary injuries. PPMM using commercially-available software (Comsol, Ansys, Matlab, CST and others) is used both at the design stage and during the treatment planning phase. However, the significant amount of PPMM data is not ready for AI/ML processing since each 4D database lack of reference to the original set of parameter (i.e. tissue properties, perfusion rate, type and location of injury…). We thus plan, within the proposed supplemental research, to address these specific aims: 1) Develop and disseminate an AI/ML-Ready PPMM dataset 2) Demonstrate the Usability of the AI/ML-Ready PPMM dataset in an AI/ML application (optimization of treatment planning) 3) Demonstrate the usability of the AI/ML-ready PPMM dataset with student engagement activities. Although the research will be focused on brain cooling PPMM, the approach will be easily expandable to other PPMM such as cancer thermal ablation, brain temperature monitoring of hypothermic cardiac surgeries and early detection of aggressive breast cancer. The proposed research will pave the way to the full potential of AI/ML technologies in tandem with multiphysics simulations for the benefit of traumatic brain injury patients.