Fichera Loris, Pardo Diego, Illiano Placido, Ortiz Jesùs, Caldwell Darwin G, Mattos Leonardo S
Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy.
Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
Int J Med Robot. 2016 Mar;12(1):53-61. doi: 10.1002/rcs.1656. Epub 2015 Apr 16.
The use of lasers in transoral surgery enables precise tissue incision with minimal adverse effects on surrounding structures. Nonetheless, the lack of haptic feedback during laser cutting impairs the surgeon's perception of the incision depth, potentially leading to undesired tissue damage.
This paper presents a novel approach, based on statistical regression analysis, to estimate the laser incision depth in soft tissue. User trials were conducted in a laser surgery set-up, to verify the effectiveness of online estimation of incision depth in supporting precise tissue cutting.
The estimation accuracy was verified on ex vivo muscle tissue, revealing a root mean squared error (RMSE) of 0.1 mm for depths ranging up to 1.4 mm. Online estimation of depth has the potential to significantly improve the incision control of users.
The proposed approach was successful in producing estimations of laser cutting depth in ex vivo muscle tissue. Further investigation is required to validate this approach on other types of tissue. Providing depth estimation during laser cutting allows users to perform more precise incisions.
