TRD3: Data Analytics and Intelligent Systems (AI-ML-DL-Visualization)

TRD3：数据分析和智能系统（AI-ML-DL-可视化）

• 批准号: 10424949
• 负责人: JINYI QI
• 金额: $ 24.78万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-20 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10424949
• 关键词: 3-Dimensional; Address; Artificial Intelligence; Back; Biophotonics; Classification; Clinical; Clinical Data; Comprehension; Computer Assisted; Critical Care; Data; Data Analytics; Data Science; Decision Making; Decision Support Systems; Detection; Discipline of obstetrics; Disease; Electroencephalography; Expert Systems; Feedback; Human; Image; Image Analysis; Individual; Intelligence; Intensive Care; Intervention; Intuition; Machine Learning; Magnetic Resonance Imaging; Medical; Medical History; Medical Imaging; Methodology; Modality; Modeling; Monitor; Multimodal Imaging; Operative Surgical Procedures; Optical Instrument; Optics; Outcome; PET/CT scan; Pathologic; Pathologist; Patient-Focused Outcomes; Patients; Performance; Physicians; Positron-Emission Tomography; Postoperative Period; Procedures; Qi; Radiology Specialty; Research; Resource Sharing; Sampling; Science; Series; Surgeon; System; Techniques; Technology; Time; Visual; Visualization; Visualization software; advanced analytics; analytical method; artificial intelligence algorithm; base; blood flow measurement; clinical care; clinical decision-making; data acquisition; data integration; data modeling; data streams; data visualization; deep learning; design; fluorescence lifetime imaging; heterogenous data; image processing; imaging system; improved; individual patient; instrument; instrumentation; machine learning model; monitoring device; multimodal data; multimodality; optical imaging; optimal treatments; personalized care; personalized intervention; personalized medicine; radiologist; response; signal processing; synergism; technology research and development; tool

PROJECT SUMMARY – Technology Research and Development Project #3 There are unmet needs in critical clinical care scenarios (e.g., surgery and intensive care) namely the lack of real-time intraprocedural imaging and pathologic data, intelligent systems for visualization, and integration this multimodality data with other clinical data for real-time decision guidance. TRD3 will develop deep learning (DL), machine learning (ML), artificial intelligence (AI), and visualization (VIS) tools to address these challenges. To accomplish this objective the research team will undertake four Specific Aims: In Aim 1, the research team will build data-driven instruments by jointly optimizing the optical hardware and the back-end machine learning model for a given task. Optimized iFLIM and iDOS instruments with increased capabilities and higher SNR will be developed for clinical use. In Aim 2, the research team will develop effective and expressive visualization interfaces and human comprehension of multimodality imaging data. These tools will provide critical information for critical decision making and improve clinical workflow. In Aim 3, the research team will develop new AI tools to integrate heterogenous multimodality data to predict patient outcome. The multi-model data integration approach will overcome the limitations of each single modality being considered in isolation. Finally, in Aim 4, the research team will incorporate these new techniques into clinical workflow to provide real-time feedback for surgical guidance. By accomplishing these aims the research team will develop and validate a set of advanced analytical methods with AI/ML/DL for intelligent instrument design, data/image analysis, visualization, and clinical decision making. Strong interactions and shared resources between this TRD and TRDs1 and 2 will enable performance advancements in the imaging and inference capabilities. The combination of these approaches will pave the way for choosing highly personalized treatments based on predictions of individual patient outcome.

项目摘要 – 技术研发项目#3 关键临床护理场景（例如手术和重症监护）存在未满足的需求，即缺乏 实时术中成像和病理数据、可视化智能系统以及集成 TRD3 将开发深度学习 (DL)、多模态数据与其他临床数据进行实时决策指导。 机器学习 (ML)、人工智能 (AI) 和可视化 (VIS) 工具可应对这些挑战。 为了实现这一目标，研究团队将实现四个具体目标： 在目标 1 中，研究团队将 通过联合优化光学硬件和后端机器学习模型来构建数据驱动的仪器 对于给定的任务，将具有增强的功能和更高的信噪比的优化 iFLIM 和 iDOS 仪器。 在目标 2 中，研究团队将开发有效且富有表现力的可视化。 这些工具将提供关键信息。 在目标 3 中，研究团队将开发新的人工智能工具，以做出关键决策并改善临床工作流程。 整合异构多模态数据来预测患者结果。 方法将克服孤立考虑每种单一模式的局限性。最后，在目标 4 中， 研究团队将把这些新技术融入临床工作流程中，为患者提供实时反馈 通过实现这些目标，研究团队将开发并验证一套先进的手术指导。 采用 AI/ML/DL 的分析方法，用于智能仪器设计、数据/图像分析、可视化和临床 该 TRD 与 TRD 1 和 2 之间的强有力的互动和共享资源将有助于做出决策。 这些方法的结合将提高成像和推理能力的性能进步。 为根据个体患者结果的预测选择高度个性化的治疗铺平道路。