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A model-based bone milling state identification method via force sensing for a robotic surgical system.

Kais I Al-Abdullah1, Chee Peng Lim1, Zoran Najdovski1

  • 1Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS
|February 6, 2019
PubMed
Summary

This study introduces a robotic bone milling technique to identify bone quality during surgery. The method accurately distinguishes between cortical and cancellous bone densities, aiding surgeons in real-time decision-making.

Keywords:
artificial neural network modellingbone densityforce sensingrobotic bone millingstate identification

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Area of Science:

  • Biomedical Engineering
  • Robotics in Surgery
  • Surgical Technology

Background:

  • Intraoperative bone quality assessment is crucial for robotic bone milling.
  • Current methods lack real-time feedback on bone density during milling procedures.

Purpose of the Study:

  • To develop a model-based method for identifying bone density during robotic bone milling.
  • To provide surgeons with real-time intraoperative bone quality information.

Main Methods:

  • An artificial neural network force model was developed using bone milling experiments.
  • The model estimates milling forces based on feed rate and spindle speed for varying bone densities.
  • Bone density is identified by comparing actual milling forces with model estimations.

Main Results:

  • The proposed method successfully distinguished between cortical bone and two types of cancellous bone densities.
  • Verification experiments confirmed the model's accuracy in identifying different bone densities.

Conclusions:

  • The developed method enables discrimination of various bone density layers during robotic milling.
  • This technique offers significant potential for enhancing surgical precision and outcomes in bone procedures.