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 在关键的临床护理方案（例如，手术和重症监护）中有未满足的需求 实时经术内部成像和病理数据，可视化的智能系统，并集成此事 具有其他临床数据的多模式数据用于实时决策指导。 TRD3将发展深度学习（DL）， 机器学习（ML），人工智能（AI）和可视化（VIS）工具以应对这些挑战。到 完成这一目标研究小组将实现四个具体目标：在AIM 1中，研究小组将 通过共同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 优化了具有增加功能和更高SNR的IFLIM和IDOS仪器 开发供临床使用。在AIM 2中，研究团队将开发有效且表现力的可视化 界面和人类对多模式成像数据的理解。这些工具将提供关键信息 用于关键决策和改善临床工作流程。在AIM 3中，研究团队将开发新的AI工具 整合异质的多模式数据以预测患者的结果。多模型数据集成 方法将克服孤立考虑的每个单一方式的局限性。最后，在AIM 4中 研究团队将将这些新技术纳入临床工作流程，以提供实时反馈 手术指导。通过完成这些目标，研究团队将开发并验证一组高级 具有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 决策。该TRD和TRDS1和2之间的强大互动和共享资源将启用 成像和推理功能的性能进步。这些方法的结合将 根据对个体患者结果的预测，为选择高度个性化治疗的方式铺平道路。