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Increasing Safety of a Robotic System for Inner Ear Surgery Using Probabilistic Error Modeling Near Vital Anatomy.

Neal P Dillon1, Michael A Siebold2, Jason E Mitchell1

  • 1Vanderbilt University, Department of Mechanical Engineering, Nashville, Tennessee, USA.

Proceedings of Spie--The International Society for Optical Engineering
|December 5, 2017
PubMed
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This summary is machine-generated.

Planning robotic surgery requires accounting for all errors to protect vital anatomy. This study develops a new method for calculating safety margins, improving robotic surgery precision and patient safety.

Area of Science:

  • Robotics
  • Surgical Planning
  • Medical Imaging

Background:

  • Robotic surgery demands precise pre-operative planning to avoid patient injury.
  • Uniform safety margins are inadequate due to spatially varying errors in robotic systems.
  • Previous methods focused on registration error, necessitating broader error consideration.

Purpose of the Study:

  • To extend safety margin calculations for robotic surgery.
  • To incorporate image distortion and physical robot errors into planning.
  • To develop a probabilistic algorithm for generating safety margins.

Main Methods:

  • Developed stochastic models for a bone-attached robot in otologic surgery.
  • Incorporated kinematic errors and robot deflections into safety margin calculations.
Keywords:
Robotic surgerybone millingerror modelingimage-guidancemastoidectomy

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  • Algorithm generates safety margins based on desired probability of anatomical preservation.

    • Main Results:

    • Simulations on a cadaver head CT scan demonstrated the algorithm's application.
    • Calculated safety margins around critical structures for robotic mastoidectomy.
    • Validated the extended approach for incorporating multiple error sources.

    Conclusions:

    • The developed method provides a more accurate approach to safety margin generation in robotic surgery.
    • This enhances the safety and effectiveness of pre-operative planning for procedures like mastoidectomy.
    • The probabilistic framework accounts for complex error sources, leading to optimized surgical plans.