Model-Based Compensation of Moving Tissue for State Recognition in Robotic-Assisted Pedicle Drilling

Drilling is one of the hardest parts of pedicle screw fixation, and it is one of the most dangerous operations because inaccurate screw placement would injury vital tissues, particularly when the vertebra is not stationary. Here we demonstrate the drilling state recognition method for moving tissue by compensating the displacement based on a simplified motion predication model of a vertebra with respect to the tidal volume. To adapt it to different patients, the prediction model was built based on the physiological data recorded from subjects themselves. In addition, the spindle speed of the drilling tool was investigated to find a suitable speed for the robotic-assisted system. To ensure patient safety, a monitoring system was built based on the thrusting force and tracked position information. Finally, experiments were carried out on a fresh porcine lamellar bone fixed on a 3-PRS parallel robot used to simulate the vertebra displacement. The success rate of the robotic-assisted drilling procedure reached 95% when the moving bone was compensated.

[1]  M. Mellema.  Ventilator waveforms. , 2013, Topics in companion animal medicine.

[2]  Yu Sun,et al.  Robot-Assisted Decompressive Laminectomy Planning Based on 3D Medical Image , 2018, IEEE Access.

[3]  Yuan Xue,et al.  Milling State Identification Based on Vibration Sense of a Robotic Surgical System , 2016, IEEE Transactions on Industrial Electronics.

[4]  G. Rosati,et al.  A haptic system for robotic assisted spine surgery , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[5]  Jianwei Zhang,et al.  A model of vertebral motion and key point recognition of drilling with force in robot-assisted spinal surgery , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[6]  Wan Kyun Chung,et al.  Human-guided surgical robot system for spinal fusion surgery: CoRASS , 2008, 2008 IEEE International Conference on Robotics and Automation.

[7]  Byung-Ju Yi,et al.  Development of SPINEBOT for spine surgery , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[8]  Li Wang,et al.  State recognition of decompressive laminectomy with multiple information in robot-assisted surgery , 2020, Artif. Intell. Medicine.

[9]  K. Kaneda,et al.  Effects of Degeneration on the Elastic Modulus Distribution in the Lumbar Intervertebral Disc , 1996, Spine.

[10]  A G Patwardhan,et al.  Pedicle Morphology of the Immature Thoracolumbar Spine , 2000, Spine.

[11]  Jianwei Zhang,et al.  Safety analysis and control of a robotic spinal surgical system , 2014 .

[12]  Stefano Stramigioli,et al.  Clinical Pedicle Screw Accuracy and Deviation From Planning in Robot-Guided Spine Surgery: Robot-Guided Pedicle Screw Accuracy , 2015, Spine.

[13]  Yuan Xue,et al.  Noncontact Vibration Measurement Based Thoracic Spine Condition Monitoring During Pedicle Drilling , 2014, IEEE/ASME Transactions on Mechatronics.

[14]  Wei Tian,et al.  Assessment of respiration‐induced vertebral motion in prone‐positioned patients during general anaesthesia , 2016, The international journal of medical robotics + computer assisted surgery : MRCAS.

[15]  T Ortmaier,et al.  Experiments on robot‐assisted navigated drilling and milling of bones for pedicle screw placement , 2006, The international journal of medical robotics + computer assisted surgery : MRCAS.

[16]  Jianwei Zhang,et al.  Compliance control based on PSO algorithm to improve the feeling during physical human–robot interaction , 2016, Robotics and biomimetics.

[17]  P. Zysset,et al.  Nanoindentation discriminates the elastic properties of individual human bone lamellae under dry and physiological conditions. , 2002, Bone.

[18]  I. Lieberman,et al.  Robotic-assisted pedicle screw placement: lessons learned from the first 102 patients , 2013, European Spine Journal.

[19]  Veit Rohde,et al.  Unskilled unawareness and the learning curve in robotic spine surgery , 2015, Acta Neurochirurgica.

[20]  Guillaume Zahnd,et al.  Cutting Depth Monitoring Based on Milling Force for Robot-Assisted Laminectomy , 2020, IEEE Transactions on Automation Science and Engineering.

[21]  M. Hardenbrook,et al.  Clinical Acceptance and Accuracy Assessment of Spinal Implants Guided With SpineAssist Surgical Robot: Retrospective Study , 2010, Spine.

[22]  A. Aliverti.  Lung and chest wall mechanics during exercise: Effects of expiratory flow limitation , 2008, Respiratory Physiology & Neurobiology.

[23]  Stephen J Ferguson,et al.  Thoracolumbar spine model with articulated ribcage for the prediction of dynamic spinal loading. , 2016, Journal of biomechanics.

[24]  Tobias Ortmaier,et al.  A hands-on-robot for accurate placement of pedicle screws , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[25]  J. Lyszczarz,et al.  [Mechanical properties of the lungs]. , 1971, Acta physiologica Polonica.

[26]  Nassir Navab,et al.  Automatic Normal Positioning of Robotic Ultrasound Probe Based Only on Confidence Map Optimization and Force Measurement , 2020, IEEE Robotics and Automation Letters.

[27]  Mamoru Mitsuishi,et al.  Hand-Held Bone Cutting Tool With Autonomous Penetration Detection for Spinal Surgery , 2015, IEEE/ASME Transactions on Mechatronics.

[28]  Edouard Laroche,et al.  Active Stabilization for Robotized Beating Heart Surgery , 2011, IEEE Transactions on Robotics.

[29]  S. Gumina,et al.  Pedicle instrumentation in the thoracic spine. A morphometric and cadaveric study for placement of screws. , 1999, Spine.

[30]  Yu Sun,et al.  Model-based spinal deformation compensation in robot-assisted decompressive laminectomy , 2019, Mechatronics (Oxford).

[31]  M. Wiesmann,et al.  Spinal Cord Motion: Influence of Respiration and Cardiac Cycle , 2014, Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren.

[32]  Rui Cortesão,et al.  Robot Force Control on a Beating Heart , 2017, IEEE/ASME Transactions on Mechatronics.

[33]  Florian Roser,et al.  Spinal robotics: current applications and future perspectives. , 2013, Neurosurgery.

[34]  Yuan Xue,et al.  Vibration-Based Milling Condition Monitoring in Robot-Assisted Spine Surgery , 2015, IEEE/ASME Transactions on Mechatronics.

[35]  Edouard Laroche,et al.  GyroLock: Stabilizing the Heart With Control Moment Gyroscope (CMG)—From Concept to First In Vivo Assessments , 2012, IEEE Transactions on Robotics.

[36]  R. Brand,et al.  Three-dimensional flexibility and stiffness properties of the human thoracic spine. , 1976, Journal of biomechanics.

[37]  Wan Kyun Chung,et al.  Automated surgical planning and evaluation algorithm for spinal fusion surgery with three-dimensional pedicle model , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[38]  Jianwei Zhang,et al.  State Recognition of Pedicle Drilling With Force Sensing in a Robotic Spinal Surgical System , 2014, IEEE/ASME Transactions on Mechatronics.

[39]  Lin Tang,et al.  Combining AHP with GIS in synthetic evaluation of eco-environment quality - A case study of Hunan Province, China , 2007 .