This technology focuses on improving spinal cord stimulation (SCS) treatments by helping physicians determine the optimal placement of stimulation leads on the body. The approach utilizes a specialized algorithm designed to predict the best lead locations for achieving effective pain relief and improved clinical outcomes. By integrating advanced imaging and electrophysiological modeling, the system provides tailored recommendations based on individual patient anatomy and physiology. The technology employs an MR-based electrophysiological model to simulate and analyze potential lead placements. This allows healthcare providers to plan treatments with greater precision, reducing the need for trial-and-error adjustments during procedures. The algorithm has the potential to enhance the effectiveness of SCS therapy, offering a more personalized and efficient solution for patients managing chronic pain. Background: Chronic pain affects millions of individuals worldwide, often requiring advanced treatments like spinal cord stimulation (SCS). However, a major challenge in SCS therapy is determining the optimal placement of stimulation to achieve effective pain relief. Current methods rely heavily on trial-and-error approaches during surgery, leading to variability in outcomes, longer procedure times, and patient discomfort. These methods also often fail to account for individual differences in anatomy and physiological response, which can reduce the overall efficacy of the treatment. This technology addresses these issues by providing a data-driven solution that predicts the best lead placement using an MR-based electrophysiological model. Unlike traditional approaches, it eliminates much of the guesswork by tailoring lead placement to the patient’s unique anatomical and neurological characteristics. By improving precision and reducing procedural time, this technology enhances clinical outcomes, minimizes risks, and increases patient satisfaction with SCS therapy. Applications:
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