Real-Time Prediction of Temperature Elevation During Robotic Bone Drilling Using the Torque Signal

Real-Time Prediction of Temperature Elevation During Robotic Bone Drilling Using the Torque Signal Bone drilling is a surgical procedure commonly required in many surgical fields, particularly orthopedics, dentistry and head and neck surgeries. While the long-term effects of thermal bone necrosis are unknown, the thermal damage to nerves in spinal or otolaryngological surgeries might lead to partial paralysis. Previous models to predict the temperature elevation have been suggested, but were not validated or have the disadvantages of computation time and complexity which does not allow real time predictions. Within this study, an analytical temperature prediction model is proposed which uses the torque signal of the drilling process to model the heat production of the drill bit. A simple Green’s disk source function is used to solve the three dimensional heat equation along the drilling axis. Additionally, an extensive experimental study was carried out to validate the model. A custom CNC-setup with a load cell and a thermal camera was used to measure the axial drilling torque and force as well as temperature elevations. Bones with different sets of bone volume fraction were drilled with two drill bits ( $$\emptyset$$ ∅ 1.8 mm and $$\emptyset$$ ∅ 2.5 mm) and repeated eight times. The model was calibrated with 5 of 40 measurements and successfully validated with the rest of the data ( $${R^{2}}=0.9034,\, SEE=7.582\,^{\circ }$$ R 2 = 0.9034 , S E E = 7.582 ∘ C). It was also found that the temperature elevation can be predicted using only the torque signal of the drilling process. In the future, the model could be used to monitor and control the drilling process of surgeries close to vulnerable structures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Biomedical Engineering Springer Journals

Real-Time Prediction of Temperature Elevation During Robotic Bone Drilling Using the Torque Signal

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Publisher
Springer US
Copyright
Copyright © 2017 by Biomedical Engineering Society
Subject
Biomedicine; Biomedicine, general; Biomedical Engineering; Biological and Medical Physics, Biophysics; Classical Mechanics; Biochemistry, general
ISSN
0090-6964
eISSN
1573-9686
D.O.I.
10.1007/s10439-017-1845-1
Publisher site
See Article on Publisher Site

Abstract

Bone drilling is a surgical procedure commonly required in many surgical fields, particularly orthopedics, dentistry and head and neck surgeries. While the long-term effects of thermal bone necrosis are unknown, the thermal damage to nerves in spinal or otolaryngological surgeries might lead to partial paralysis. Previous models to predict the temperature elevation have been suggested, but were not validated or have the disadvantages of computation time and complexity which does not allow real time predictions. Within this study, an analytical temperature prediction model is proposed which uses the torque signal of the drilling process to model the heat production of the drill bit. A simple Green’s disk source function is used to solve the three dimensional heat equation along the drilling axis. Additionally, an extensive experimental study was carried out to validate the model. A custom CNC-setup with a load cell and a thermal camera was used to measure the axial drilling torque and force as well as temperature elevations. Bones with different sets of bone volume fraction were drilled with two drill bits ( $$\emptyset$$ ∅ 1.8 mm and $$\emptyset$$ ∅ 2.5 mm) and repeated eight times. The model was calibrated with 5 of 40 measurements and successfully validated with the rest of the data ( $${R^{2}}=0.9034,\, SEE=7.582\,^{\circ }$$ R 2 = 0.9034 , S E E = 7.582 ∘ C). It was also found that the temperature elevation can be predicted using only the torque signal of the drilling process. In the future, the model could be used to monitor and control the drilling process of surgeries close to vulnerable structures.

Journal

Annals of Biomedical EngineeringSpringer Journals

Published: May 5, 2017

References

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