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Robot-assisted perception augmentation for online detection of insertion failure during cochlear implant surgery

Published online by Cambridge University Press:  09 June 2016

J. Pile ,
G. B. Wanna  and
N. Simaan
J. Pile
Affiliation:
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37235USA. E-mail: [email protected]
G. B. Wanna
Affiliation:
Department of Otolaryngology Head & Neck Surgery, Vanderbilt University, Nashville, TN 37235USA. E-mail: [email protected]
N. Simaan*
Affiliation:
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37235USA. E-mail: [email protected] Department of Otolaryngology Head & Neck Surgery, Vanderbilt University, Nashville, TN 37235USA. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

During the past decade, robotics for cochlear implant electrode array insertion has been limited to manipulation assistance. Going beyond manipulation assistance, this paper presents the new concept of perception augmentation to detect and warn against the onset of intracochlear electrode array tip folding. This online failure detection method uses a combination of intraoperative electrode insertion force data and a predictive model of insertion force profile progression as a function of insertion depth. The predictive model uses statistical characterization of insertion force profiles during normal robotic electrode array insertions as well as the history of intra-operative insertion forces. Online detection of onset of tip folding is achieved using the predictive model as an input into a support vector machine classifier. Results show that the detection of tip folding onset can be achieved with an accuracy of 88% despite the use of intra-operative insertion force data representing incomplete insertion. This result is significant because it allows the surgeon or robot to choose a corrective action for preventing intra-cochlear complications.

Keywords

cochlear implantsfault detectionmedical robotics
Type
Articles
Information
Robotica , Volume 35 , Issue 7 , July 2017 , pp. 1598 - 1615
Copyright
Copyright © Cambridge University Press 2016 

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