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Development of a Transoral Robotic Surgery Training Platform

Rory Geoghegan, Jonathan Song, Aadesh P. Singh, Tyler Le, Ahmad Abiri, Abie H. Mendelsohn

Year
2019
Citations
3

Abstract

Transoral robotic surgery (TORS) presents unique challenges due to difficulty manipulating surgical instruments within the tight confines of the oral cavity. Collisions between the end effectors and anatomical structures can be visualized through the endoscope; however, instrument shaft collisions are outside of the field-of-view. Acquiring the requisite skill set to minimize these collisions is challenging due to the lack of an appropriate training platform. In this paper, we present a TORS training platform with an integrated collision sensing system and real-time haptic feedback. Preliminary testing involved the recruitment of 10 Otolaryngology residents assigned to `feedback' (N=5) and `no feedback' (N=5) groups. Each trainee performed three mock surgical procedures involving the resection of a tumor from the base of the tongue. Superior surgical performance was observed in the feedback group suggesting that haptic feedback will enhance the acquisition of surgical skills.

Keywords

Transoral robotic surgeryComputer scienceRobotic surgeryTraining (meteorology)RobotMedical roboticsHuman–computer interactionArtificial intelligenceMedicineSurgery

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