Specific Evaluation of Lesion Categories

Multiple Sclerosis (MS) is a debilitating disease that primarily affects young individuals. Disease Modifying Therapies (DMT) developed in the past two decades have greatly improved the quality of life for people living with MS. Assessment of brain lesions using Magnetic Resonance Imaging (MRI) has been a standard method to evaluate the efficacy of DMT in clinical trials and practice. However, standard techniques do not differentiate between the different stages of lesion evolution. Using advanced MRI and machine learning techniques, this project aims to develop an automated technique for Specific Evaluation of LEsion CaTegories (SELECT), with the goal of improving lesion assessment in clinical trials and practice. Building on his previous experience evaluating MS deep gray matter using machine learning, the intern will acquire valuable expertise in developing and applying machine learning techniques to MS lesions. The partner organization will benefit from the interns previous experience with MS machine learning techniques.

Faculty Supervisor:

David Rudko

Student:

Ahmed Elkady

Partner:

NeuroRx Research Inc.

Discipline:

Medicine

Sector:

Medical devices

University:

Program: