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    DGPC-Net accurately predicts muscle activation from spinal cord stimulation (SCS). This AI tool enhances treatment efficiency by reducing parameter optimization time for conditions like Parkinson's disease.

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    Area of Science:

    • Biomedical Engineering
    • Computational Neuroscience
    • Machine Learning

    Background:

    • Spinal cord stimulation (SCS) is a therapeutic approach for neurological conditions.
    • Optimizing SCS parameters for individual patients is crucial but time-consuming.
    • Predicting muscle activation is key to effective SCS.

    Purpose of the Study:

    • To introduce DGPC-Net, a novel dual-branch Gaussian Process Constrained Network.
    • To predict muscle activation intensity in response to SCS.
    • To improve the efficiency of SCS parameter optimization.

    Main Methods:

    • Developed DGPC-Net, a dual-branch Gaussian Process Constrained Network.
    • Utilized MRI scans, finite element, and NEURON simulations for personalized virtual environments.
    • Generated simulated muscle activation data for epidural electrical stimulation (EES).

    Main Results:

    • DGPC-Net achieved 81.42% balanced accuracy in binary muscle activation classification.
    • Demonstrated a strong correlation of 0.93 between predicted and ground-truth muscle activation intensities.
    • Ablation studies validated the necessity of each network component.

    Conclusions:

    • DGPC-Net accurately predicts muscle activation intensity from SCS.
    • Real-time prediction capability can significantly reduce SCS parameter optimization time.
    • This approach holds potential for improving treatment efficiency for spinal cord injury and Parkinson's disease gait issues.