Advancing Spinal Cord Injury Treatment Through AI

|Research & Development
2D MRI imagery and 3D visualization comparison for spinal cord stimulation

2D MRI imagery (left) converted into 3D visualizations (right) to assist surgeons in implant placement. Credit: Johns Hopkins APL/Jordan Matelsky

Project Overview

At Johns Hopkins Applied Physics Laboratory, I'm part of a team developing innovative solutions for people living with spinal cord injuries. Our work focuses on spinal cord epidural stimulation (scES), a promising technology that can help restore motor control and autonomic function to patients.

Technical Challenges

The project addresses two critical technological gaps in scES treatment:

  • Precise electrode placement visualization for surgeons
  • Dynamic control systems for stimulation parameters

My Role & Contributions

As a neuralinterface research scientist on the project, I work alongside Erik Johnson to develop:

  • An Android tablet application for wireless communication with implanted scES systems
  • User interfaces for patient control and feedback
  • Integration with sensory hardware and stimulation systems

Innovation in Patient Care

Our work is particularly focused on incorporating patient feedback into the treatment loop. This is crucial for transitioning the technology from clinical settings to home use, where patients need to manage their own treatment effectively.

Collaborative Effort

This project represents a collaboration between multiple institutions:

  • Johns Hopkins Applied Physics Laboratory - Technical development
  • University of Louisville - Clinical expertise and training data
  • Kessler Institute - Clinical partnership
  • Medtronic Inc. - Medical device manufacturing

Future Directions

Our team continues to work on:

  • Incorporating more stimulation parameters into the control algorithms
  • Improving robustness against physical disturbances
  • Addressing critical issues like orthostatic hypotension
  • Enhancing the patient feedback loop

Learn More

This work was presented at the 11th International IEEE Engineering in Medicine and Biology Society's Conference on Neural Engineering. For more information, you can read theofficial news release from JHU APL.