Presentation
9 September 2019 Developing an intelligent read/write link to the spinal cord (Conference Presentation)
David A. Borton
Author Affiliations +
Abstract
The number of people with spinal cord injury living in the United States is currently estimated to be approximately 288,000 persons and about 42,000 are veterans. Spine injuries are more prevalent among Operation Iraqi Freedom and Operation Enduring Freedom veterans than among veterans of prior conflicts due to advances in body armor making injuries more survivable. Today, activity-based therapies are the only medical practices that can be used to enhance recovery after spinal cord injury. Several studies have now shown that spinal cord stimulation delivered at the right time can enhance a physical therapy rehabilitation program significantly, leading to restoration of volitional walking. Here, we will discuss efforts to develop a bi-directional tool for sensing and stimulating the spinal cord in order to bridge a gap ‘reconnect’ patches of eloquent tissue, without the need external systems. Our platform innovates on the use of high-density electrode arrays; the use of state-of-the-art artificial neural network designs, optimization methods, and neural network-accelerated hardware targets; and layout of a device regulatory pathway for fully implanted system for advanced future therapies. Such technological demonstration of a spine-machine-spine interface will be of immediate practical and therapeutic utility to the wounded warrior with SCI. More generally, such technology could be used across other axes of spinal cord injury, including chronic pain and achieve additive therapeutic outcomes.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David A. Borton "Developing an intelligent read/write link to the spinal cord (Conference Presentation)", Proc. SPIE 11087, Biosensing and Nanomedicine XII, 110870A (9 September 2019); https://doi.org/10.1117/12.2532475
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KEYWORDS
Spinal cord

Injuries

Therapeutics

Artificial neural networks

Surgery

Bridges

Electrodes

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