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Bioimpedance Sensing Surgical Drill - Computational Modelling and Experimental Validation.

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    Dental implant surgery risks nerve and sinus injury. This study developed a simulation to optimize electrical impedance sensing for real-time drill guidance, enhancing surgical safety.

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    Area of Science:

    • Biomedical Engineering
    • Surgical Technology
    • Electrical Impedance Tomography

    Background:

    • Dental implant placement carries a significant risk of iatrogenic injury to the inferior alveolar nerve (IAN) and maxillary sinus.
    • Current surgical planning relies on pre-operative imaging, lacking real-time feedback during drilling to prevent critical structure damage.

    Purpose of the Study:

    • To investigate the feasibility of using electrical impedance measurements from the drill tip for real-time proximity sensing of the IAN and maxillary sinus.
    • To optimize sensor geometry for maximizing detection distance in electrical impedance sensing during dental drilling.

    Main Methods:

    • Development of a finite element method (FEM) based simulation platform.
    • Simulation of electrical impedance properties across various tissue conductivities, frequencies, and sensor geometries.
    • Analysis of sensor geometry optimization for enhanced detection capabilities.

    Main Results:

    • The FEM simulation platform accurately predicts tissue impedances.
    • Identified optimal sensor geometries for improved detection distance.
    • Demonstrated the potential for impedance sensing to provide real-time feedback on drill proximity to critical anatomical structures.

    Conclusions:

    • Electrical impedance sensing from the drill tip offers a promising approach for real-time guidance during dental implant surgery.
    • Optimized sensor design is crucial for effective proximity detection, potentially reducing the risk of nerve and sinus injury.
    • This simulation platform provides a foundation for developing advanced intraoperative safety systems.