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
- 来源:
- 关键词:AcuteAlgorithmsArtificial IntelligenceAttentionCaringCerebral InfarctionCerebrovascular DisordersCessation of lifeClinicalClinical DataClinical TrialsCollaborationsComputer softwareDataDecision MakingDisease ProgressionDisparateEtiologyFunctional disorderGoalsHemorrhageImageIndividualInfarctionInterventionIschemiaIschemic StrokeLeadLearningLocationMagnetic Resonance ImagingMapsMedicalMedicineMethodsMinorModelingOutcomePathway interactionsPatient SelectionPatientsPerformancePerfusionPopulationProcessProspective cohortProtocols documentationReperfusion TherapyResearch PersonnelRiskSafetySiteSpeedStrokeTechniquesTestingThrombectomyTimeTissuesTrainingTriageWorkX-Ray Computed Tomographyacute strokeartificial intelligence methodclinical practicecohortconvolutional neural networkcostdata streamsdeep learningdeep learning modeldeep neural networkendovascular thrombectomyexperiencefollow-upfunctional outcomesfunctional statusimaging studyimprovedlearning strategyneural networkneuroimagingoutcome predictionpatient populationperfusion imagingpredictive modelingrandomized, clinical trialsstroke patientstroke trialstooltransfer learningtreatment effect
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
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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Gregory George Zaharchuk其他文献
Gregory George Zaharchuk的其他文献
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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
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9789276 - 财政年份:2018
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Cerebrovascular Reserve Imaging with Simultaneous PET/MRI Using Arterial Spin Labeling and Deep Learning
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8827866 - 财政年份:2014
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$ 62.82万 - 项目类别:
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缺血性中风的 MRI 氧合指纹图谱
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$ 62.82万 - 项目类别:
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急性中风的侧枝循环成像 (iCAS)
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