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Markerless Navigation in Computer Assisted Orthopaedic Surgery Using RGB-D Data: a Phantom-Based Comparative Study of

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    Markerless 3D localization using deep learning shows promise for computer-assisted orthopedic surgery (CAOS). This approach, utilizing RGB-D data, achieved lower errors than traditional methods, potentially reducing invasiveness and operation time.

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    Area of Science:

    • Orthopedic Surgery
    • Computer Vision
    • Medical Robotics

    Background:

    • Computer Assisted Orthopedic Surgery (CAOS) enhances surgical outcomes.
    • Current navigation systems rely on optical markers, which have drawbacks limiting widespread adoption.
    • Markers increase operation duration and invasiveness.

    Purpose of the Study:

    • To evaluate markerless 3D localization algorithms for orthopedic surgical objects using RGB-D data.
    • To compare a deep-learning (DL)-based algorithm against the Point-Pair Features (PPF) algorithm.
    • To assess the potential for markerless navigation in CAOS.

    Main Methods:

    • Development of a surgical-based database for algorithm evaluation.
    • Implementation and testing of a DL-based algorithm for object localization.
    • Comparison of DL algorithm performance against the PPF algorithm using the created database.

    Main Results:

    • The DL-based algorithm achieved a global mean error of 1.28 mm in translation and 1.54° in rotation.
    • The DL-based algorithm outperformed the PPF algorithm in accuracy.
    • Errors require further reduction, but results are promising for markerless navigation.

    Conclusions:

    • Deep learning-based algorithms show significant potential for markerless 3D localization in CAOS.
    • Exploiting RGB-D data can reduce reliance on markers, leading to less invasive and shorter surgeries.
    • Further development of DL models could pave the way for advanced markerless surgical navigation systems.