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A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
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A CT Image-Based Virtual Sensing Method to Estimate Bone Drilling Force for Surgical Robots.

Liang Li, Sheng Yang, Wuke Peng

    IEEE Transactions on Bio-Medical Engineering
    |August 30, 2021
    PubMed
    Summary

    This study introduces a novel method for surgical robots to predict bone drilling forces using preoperative images. This enhances robotic surgical precision and efficiency by providing crucial a priori force information.

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    Area of Science:

    • Robotics
    • Biomedical Engineering
    • Medical Imaging

    Background:

    • Surgical robots lack the ability to interpret preoperative images like human surgeons.
    • This limits their capacity to leverage preoperative data for stable and efficient operations.
    • Bone drilling tasks in surgery require precise force control.

    Purpose of the Study:

    • To develop a method for estimating drilling force information from preoperative images.
    • To provide surgical robots with a priori force data for bone drilling tasks.
    • To improve the stability and efficiency of robotic surgery.

    Main Methods:

    • A visual sensing computing framework was developed to process 3D image data into a 1D signal.
    • A computed tomography (CT) image-weighted mechanical model was created for bone drilling.
    • The model incorporates bone shape, material properties, and CT image data to predict forces.

    Main Results:

    • The model accurately predicts thrust force, torque, and radial force during bone drilling.
    • It accounts for various factors including surgical plans, tissue density, and drill bit geometry.
    • Achieved low prediction errors on bovine and porcine bone models, demonstrating potential in spinal surgery.

    Conclusions:

    • The developed method effectively predicts bone drilling forces using preoperative images.
    • This provides surgical robots with enhanced preoperative information for improved performance.
    • Offers a new approach to understanding robot-tissue interactions in surgery.