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    This study introduces a fast fiber shape sensing method using a two-layer long short-term memory (LSTM) network for accurate visualization and navigation of surgical instruments. The approach achieves real-time performance with minimal error, enhancing minimally invasive surgery.

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    Area of Science:

    • Medical Engineering
    • Robotics
    • Artificial Intelligence

    Background:

    • Minimally invasive surgery requires precise visualization and navigation of instruments in confined spaces.
    • Traditional methods face limitations due to electromagnetic interference and ionizing radiation.

    Purpose of the Study:

    • To develop a fast and accurate fiber shape sensing approach for surgical instrument navigation.
    • To overcome the limitations of existing navigation techniques.

    Main Methods:

    • Utilized a two-layer long short-term memory (LSTM) network for shape reconstruction.
    • Directly regressed 3D coordinates from fiber Bragg grating (FBG) wavelength shifts, bypassing curvature estimation.
    • Implemented real-time inference with low end-to-end latency.

    Main Results:

    • Achieved real-time inference at 32 frames per second with a 7.3 ms latency.
    • Demonstrated a mean tip error of 2.9 mm (0.72% of sensing length).
    • Validated high-speed and high-accuracy fiber shape reconstruction.

    Conclusions:

    • The proposed LSTM-based method offers a viable solution for real-time fiber shape sensing.
    • This technology has significant potential for enhancing real-time navigation in minimally invasive procedures.
    • The approach addresses key challenges in surgical instrument tracking.