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)
基本信息
- 批准号:10424949
- 负责人:
- 金额:$ 24.78万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:3-DimensionalAddressArtificial IntelligenceBackBiophotonicsClassificationClinicalClinical DataComprehensionComputer AssistedCritical CareDataData AnalyticsData ScienceDecision MakingDecision Support SystemsDetectionDiscipline of obstetricsDiseaseElectroencephalographyExpert SystemsFeedbackHumanImageImage AnalysisIndividualIntelligenceIntensive CareInterventionIntuitionMachine LearningMagnetic Resonance ImagingMedicalMedical HistoryMedical ImagingMethodologyModalityModelingMonitorMultimodal ImagingOperative Surgical ProceduresOptical InstrumentOpticsOutcomePET/CT scanPathologicPathologistPatient-Focused OutcomesPatientsPerformancePhysiciansPositron-Emission TomographyPostoperative PeriodProceduresQiRadiology SpecialtyResearchResource SharingSamplingScienceSeriesSurgeonSystemTechniquesTechnologyTimeVisualVisualizationVisualization softwareadvanced analyticsanalytical methodartificial intelligence algorithmbaseblood flow measurementclinical careclinical decision-makingdata acquisitiondata integrationdata modelingdata streamsdata visualizationdeep learningdesignfluorescence lifetime imagingheterogenous dataimage processingimaging systemimprovedindividual patientinstrumentinstrumentationmachine learning modelmonitoring devicemultimodal datamultimodalityoptical imagingoptimal treatmentspersonalized carepersonalized interventionpersonalized medicineradiologistresponsesignal processingsynergismtechnology research and developmenttool
项目摘要
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.
项目概要-技术研究与开发项目#3
在重症临床护理场景中存在未满足的需求(例如,手术和重症监护),即缺乏
实时术中成像和病理数据,可视化智能系统,
多模态数据与其他临床数据进行实时决策指导。TRD 3将开发深度学习(DL),
机器学习(ML)、人工智能(AI)和可视化(维斯)工具来应对这些挑战。到
为了实现这一目标,研究小组将承担四个具体目标:在目标1中,研究小组将
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。经过优化的iFLIM和iDOS仪器将具有更强的功能和更高的SNR,
为临床使用而开发。在目标2中,研究团队将开发有效且富有表现力的可视化
多模态成像数据的接口和人类理解。这些工具将提供关键信息
进行关键决策并改善临床工作流程。在Aim 3中,研究团队将开发新的AI工具
整合异质多模态数据以预测患者结局。多模型数据集成
这种方法将克服孤立考虑每一种单一模式的局限性。最后,在目标4中,
研究小组将把这些新技术纳入临床工作流程,
手术指导通过实现这些目标,研究小组将开发和验证一套先进的
AI/ML/DL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。本TRD与TRD 1和TRD 2之间的强有力的互动和共享资源将使
成像和推理能力的性能提升。这些方法的结合将
为基于个体患者结果的预测选择高度个性化的治疗铺平道路。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('JINYI QI', 18)}}的其他基金
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:
10649478 - 财政年份:2022
- 资助金额:
$ 24.78万 - 项目类别:
Positronium lifetime imaging using TOF PET
使用 TOF PET 进行正电子寿命成像
- 批准号:
10288242 - 财政年份:2021
- 资助金额:
$ 24.78万 - 项目类别:
Positronium lifetime imaging using TOF PET
使用 TOF PET 进行正电子寿命成像
- 批准号:
10443873 - 财政年份:2021
- 资助金额:
$ 24.78万 - 项目类别:
Synergistic integration of deep learning and regularized image reconstruction for positron emission tomography
深度学习与正电子发射断层扫描正则化图像重建的协同集成
- 批准号:
9586688 - 财政年份:2018
- 资助金额:
$ 24.78万 - 项目类别:
Synergistic integration of deep learning and regularized image reconstruction for positron emission tomography
深度学习与正电子发射断层扫描正则化图像重建的协同集成
- 批准号:
9752639 - 财政年份:2018
- 资助金额:
$ 24.78万 - 项目类别:
Iterative Image reconstruction for high-resolution PET imaging
高分辨率 PET 成像的迭代图像重建
- 批准号:
7383846 - 财政年份:2007
- 资助金额:
$ 24.78万 - 项目类别:
Iterative Image reconstruction for high-resolution PET imaging
高分辨率 PET 成像的迭代图像重建
- 批准号:
7265565 - 财政年份:2007
- 资助金额:
$ 24.78万 - 项目类别:
Iterative Image reconstruction for high-resolution PET imaging
高分辨率 PET 成像的迭代图像重建
- 批准号:
7586255 - 财政年份:2007
- 资助金额:
$ 24.78万 - 项目类别:
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