Predicting Tissue and Functional Outcome in Acute Stroke

预测急性中风的组织和功能结果

基本信息

  • 批准号:
    10568740
  • 负责人:
  • 金额:
    $ 62.82万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-08-15 至 2028-07-31
  • 项目状态:
    未结题

项目摘要

Abstract Stroke is a disabling cerebrovascular disease that causes 5.5 million deaths each year globally. The disease progresses rapidly and irreversibly, leaving a narrow time window for intervention. Existing methods for patient selection for endo- vascular thrombectomy are suboptimal, based exclusively on simple linear threshold models applied to neuroimaging. Deep learning has shown great promise in recent years for many medical applications. We believe that it can be used to integrate imaging and non-imaging data in a seamless and data- driven way to improve stroke triage and clinical trials. The goal of this project is to develop deep convolutional neural network approaches to the initial MR and CT imaging, the most commonly performed stroke imaging protocol in acute ischemic stroke patients, and to combine this with non-imaging clinical information. We will train networks to predict the most likely final tissue and clinical outcomes under 2 extreme conditions (major reperfusion and minimal reperfusion) to estimate the treatment effect at the individual level. Next, we use the methods and learning from this first study to train deep learning models without using contrast perfusion imaging, which will improve safety, cost, and time-to-treatment. Finally, we will test the generalizability and explainability of these AI methods in external cohorts which differ in terms of population and scanner types, including testing on data from mobile CT scanners. Accomplishment of these aims will fundamentally shift the acute stroke paradigm beyond the relatively simplistic mismatch concept and replace it with a data-driven method that takes into account the immense amount of imaging and clinical data that can be brought to the stroke decision-making process. The methods developed will improve long-term outcomes and reduce of the cost of stroke care worldwide.
摘要

项目成果

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专利数量(0)

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Gregory George Zaharchuk其他文献

Gregory George Zaharchuk的其他文献

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{{ truncateString('Gregory George Zaharchuk', 18)}}的其他基金

AI-Enhanced Brain PET Imaging for Alzheimer's Disease
AI 增强型大脑 PET 成像治疗阿尔茨海默病
  • 批准号:
    10670483
  • 财政年份:
    2022
  • 资助金额:
    $ 62.82万
  • 项目类别:
Next Generation Brain PET Imaging
下一代脑 PET 成像
  • 批准号:
    10279862
  • 财政年份:
    2021
  • 资助金额:
    $ 62.82万
  • 项目类别:
Next Generation Brain PET Imaging
下一代脑 PET 成像
  • 批准号:
    10478939
  • 财政年份:
    2021
  • 资助金额:
    $ 62.82万
  • 项目类别:
Cerebrovascular Reserve Imaging with Simultaneous PET/MRI Using Arterial Spin Labeling and Deep Learning
使用动脉自旋标记和深度学习同时进行 PET/MRI 脑血管储备成像
  • 批准号:
    10181176
  • 财政年份:
    2020
  • 资助金额:
    $ 62.82万
  • 项目类别:
Cerebrovascular Reserve Imaging with Simultaneous PET/MRI Using Arterial Spin Labeling and Deep Learning
使用动脉自旋标记和深度学习同时进行 PET/MRI 脑血管储备成像
  • 批准号:
    9789276
  • 财政年份:
    2018
  • 资助金额:
    $ 62.82万
  • 项目类别:
Cerebrovascular Reserve Imaging with Simultaneous PET/MRI Using Arterial Spin Labeling and Deep Learning
使用动脉自旋标记和深度学习同时进行 PET/MRI 脑血管储备成像
  • 批准号:
    10205063
  • 财政年份:
    2018
  • 资助金额:
    $ 62.82万
  • 项目类别:
Oxygenation Fingerprinting with MRI for Ischemic Stroke
缺血性中风的 MRI 氧合指纹图谱
  • 批准号:
    8827866
  • 财政年份:
    2014
  • 资助金额:
    $ 62.82万
  • 项目类别:
Oxygenation Fingerprinting with MRI for Ischemic Stroke
缺血性中风的 MRI 氧合指纹图谱
  • 批准号:
    8684656
  • 财政年份:
    2014
  • 资助金额:
    $ 62.82万
  • 项目类别:
USING ARTERIAL SPIN LABEL AND PWI TO MEASURE QUANTITATIVE CBF
使用动脉旋转标签和 PWI 定量测量 CBF
  • 批准号:
    8362921
  • 财政年份:
    2011
  • 资助金额:
    $ 62.82万
  • 项目类别:
Imaging Collaterals in Acute Stroke (iCAS)
急性中风的侧枝循环成像 (iCAS)
  • 批准号:
    9314645
  • 财政年份:
    2009
  • 资助金额:
    $ 62.82万
  • 项目类别:

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