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Pattern Classification Using Magnetic Resonance Imaging in Traumatic Brain Injury

使用磁共振成像对创伤性脑损伤进行模式分类

基本信息

批准号：
9295067
负责人：
Yvonne W Lui
金额：
$ 19.84万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9295067
关键词：
Address Affect Algorithms Artificial Intelligence Behavior Biological Markers Biophysics Brain Brain Injuries Brain region Characteristics Classification Clinical Clinical Research Common Data Element Communication Complex Computational algorithm Computing Methodologies Connective Tissue Corpus Callosum Data Data Set Development Diagnosis Diagnostic Diffusion Disease Early Intervention Educational workshop Equipment and supply inventories Eye FarGo Functional disorder Funding Glasgow Coma Scale Goals Grant Image Imaging problem Incidence Individual Injury Iron Learning Linear Regressions Link Machine Learning Magnetic Resonance Imaging Measurement Measures Methods Modeling Motor National Institute of Neurological Disorders and Stroke Neurons Neuropsychological Tests Neuropsychology Non-linear Models Outcome Pathology Pathway interactions Patient risk Patients Pattern Performance Persons Phase Play Positioning Attribute Public Health Publishing Recording of previous events Regression Analysis Research Rest Risk Role Societies Symptoms System Techniques Testing Thalamic structure Time Tissues Trauma Traumatic Brain Injury Treatment Protocols United States United States National Institutes of Health Work axon injury base clinically relevant cohort computerized tools cost disability drug development evidence base executive function experience improved indexing magnetic field mild traumatic brain injury mood regulation neural patterning neuroimaging novel novel therapeutics outcome forecast outcome prediction predict clinical outcome predictive modeling prognostic relating to nervous system response scaffold tool white matter white matter injury

项目摘要

PROJECT SUMMARY Mild traumatic brain injury (MTBI) affects ~1.5 million persons annually in the United States with fifteen to 30% of patients suffering long-term disability after injury. We remain in the early phase of understanding this disease and one of the greatest barriers to studying the disease and developing appropriate therapy is the difficulty in diagnosis and outcome prediction. Generally, the diagnosis of MTBI relies on using the Glasgow Coma Scale (GCS), a 15-point gross measurement of eye-opening, motor and verbal response. The National Institute for Neurological Disorders and Stroke (NINDS) workshop in 2014 indicated that use of GCS score as a single classifier for TBI is insufficient and proposed that neuroimaging play a larger role towards the development of objective criteria for diagnosis and outcome prediction. We have specific experience in studying novel MRI techniques that show much promise in evaluating MTBI patients. The goal of the current proposal is to bring these novel MRI techniques to clinical use. We propose to combine information from objective MR imaging features with clinical information to learn the patterns that can best distinguish patients from controls and predict long-term outcome using machine learning. We will validate our tool using a separate subject cohort. Such a tool would be an extremely powerful clinical tool to identify at-risk patients for early intervention. Additionally, this research will identify the most clinically relevant MR metrics, thereby pointing the way to novel therapeutic pathways.

项目总结