Development of a Transoral Robotic Surgery Training Platform
Rory Geoghegan, Jonathan Song, Aadesh P. Singh, Tyler Le, Ahmad Abiri, Abie H. Mendelsohn
- 发表年份
- 2019
- 引用次数
- 3
摘要
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.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002