State-of-the-Art of Non-Radiative, Non-Visual Spine Sensing with a Focus on Sensing Forces, Vibrations and Bioelectrical Properties: A Systematic Review.

Journal: Sensors (Basel, Switzerland)
PMID:

Abstract

In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in robotics which goes beyond the standard engineering notions such as geometric precision. The objective of this review is to present an overview of the state of the art in non-visual, non-radiative spine sensing for the enhancement of surgical techniques in robotic automation. It provides a vantage point that facilitates experimentation and guides new research projects to what has not been investigated or integrated in surgical robotics. Studies were identified, selected and processed according to the PRISMA guidelines. Relevant study characteristics that were searched for include the sensor type and measured feature, the surgical action, the tested sample, the method for data analysis and the system's accuracy of state identification. The 6DOF f/t sensor, the microphone and the electromyography probe were the most commonly used sensors in each category, respectively. The performance of the electromyography probe is unsatisfactory in terms of preventing nerve damage as it can only signal after the nerve is disturbed. Feature thresholding and artificial neural networks were the most common decision algorithms for state identification. The fusion of different sensor data in the decision algorithm improved the accuracy of state identification.

Authors

  • Maikel Timmermans
    KU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium.
  • Aidana Massalimova
  • Ruixuan Li
    State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China.
  • Ayoob Davoodi
    School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
  • Quentin Goossens
    KU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium.
  • Kenan Niu
    Robotics and Mechatronics, University of Twente, Enschede, The Netherlands.
  • Emmanuel Vander Poorten
    Department of Mechanical Engineering, University of Leuven, Celestijnenlaan 300B, 3001, Heverlee, Belgium.
  • Philipp Fürnstahl
    Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Balgrist Campus, 8008, Zurich, Switzerland.
  • Kathleen Denis
    KU Leuven, Department of Mechanical Engineering, BioMechanics (BMe), Smart Instrumentation, 3000 Leuven, Belgium.