Applying deep learning to MRI and clinical data for precision medicine

 
   Lead Investigator:    Xiaotong Jiang
   Institution      :    University of North Carolina at Chapel Hill
   E-Mail           :    xiaotong.phoebe.jiang@gmail.com
   Proposal ID      :    1369



   Proposal Description:
   The goal of the proposed research is to find the optimal personalized plan for modifiable risk factors by applying deep learning techniques to MRI imaging and   patient clinical data in the NACC data. Our bodies react to treatments            differently and patient differences often make the clinical decision making       hard. When is the best time to treat what patient with what intervention?         Precision medicine is a rising area that tries to solve this kind of problem      but most of its work has been focusing on low-dimensional data. We want to        borrow the strength and flexibility of deep learning and make use of imaging      data, together with standard health record data, to help clinicians make better treatment recommendations. We believe this research will open doors to lots of    other opportunities for precision medicine to take advantage of big, complex      data. We have read through the documentation and handbook provided on this        website and found the UDS dataset as well as the MRI imaging data good sources    of our clinical application.