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

开发用于参数多物理场建模数据集的 AI/ML 就绪共享存储库：创伤后选择性脑冷却预测模型的标准化

• 批准号: 10842926
• 负责人: Paolo Francesco Maccarini
• 金额: $ 30.34万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10842926
• 关键词: 3-Dimensional; Acceleration; Address; Affect; Anatomy; Artificial Intelligence; Basic Science; Behavior; Big Data; Biomedical Research; Brain; Brain Injuries; Cancerous; Cardiac Surgery procedures; Catheters; Cerebrum; Clinical; Clinical Research; Code; Collaborations; Communities; Complex; Computer software; Data; Data Engineering; Data Files; Data Science; Data Set; Databases; Development; Development Plans; Device Designs; Devices; Documentation; Early Diagnosis; Electromagnetics; Engineering; Exploratory/Developmental Grant for Diagnostic Cancer Imaging; Funding; Future; Goals; Grant; Head; Health Care Costs; Information Technology; Injury; Intracranial Pressure; Learning Skill; Lesion; Location; Long-Term Effects; Machine Learning; Malignant Neoplasms; Maps; Medical Device; Medical Device Designs; Medicine; Modeling; Monitor; Outcome; Output; Patient-Focused Outcomes; Patients; Perfusion; Phase; Physics; Physiological; Power Sources; Prediction of Response to Therapy; Probability; Procedures; Process; Property; Public Health; Pythons; Quality of life; Readability; Research; Research Personnel; Research Support; Resource-limited setting; Running; Standardization; Students; System; TBI Patients; Technology; Temperature; Testing; Thermal Ablation Therapy; Time; Tissues; Training; Trauma; Traumatic injury; Treatment Protocols; United States National Institutes of Health; Validation; Variant; Ventricular; Work; aggressive breast cancer; behavior prediction; blood perfusion; brain tissue; clinical application; clinically relevant; complex data; data curation; data management; data modeling; data standards; deep learning model; design; experience; file format; graduate student; improved; innovation; insight; large datasets; learning community; machine learning algorithm; manufacture; microwave electromagnetic radiation; multidisciplinary; natural hypothermia; novel; open source; pre-clinical; predictive modeling; programs; real time monitoring; repository; response; sensor; shared repository; signal processing; simulation; skill acquisition; skills; student participation; therapy design; tool; treatment optimization; treatment planning; tumor; undergraduate student; usability

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.

摘要