Mild Traumatic Brain Injury (MTBI) is a growing public health problem with an underestimated incidence of over one million people annually in the U.S. Neuropsychological tests are used to both assess the patient condition and to monitor patient progress. This work aims to use features extracted from MRI images taken shortly after injury to predict whether a person has MTBI or not.
Within this task, we need to also determine the most effective features and corresponding classification method. The main challenge is that we have only limited training data, from which we need to develop the prediction method that can be expected to provide accurate prediction results for unseen data.
As a long term goal, we also plan to use data augmentation technique to enlarge the number of samples, and exploit deep learning approach to perform classification directly from MRI images.
As another goal, this work aims to use features extracted from MR images taken shortly after injury to predict the performance of MTBI patients on neuropsychological tests one year after injury. Successful prediction can enable early patient stratification and proper treatment planning.
This work is supported by the National Institute of Health
- S Minaee, Y Wang, YW Lui, "Prediction of longterm outcome of neuropsychological tests of MTBI patients using imaging features," Signal Processing in Medicine and Biology Symposium (SPMB), IEEE, 2013.