Tissue Identification Through Back End Sensing on da Vinci EndoWrist Surgical Tool1

Abstract

Background Surgical robots are becoming more common in the operating room. Although surgeons utilize these robots for improved dexterity, scalable movements, and enhanced vision, they lose their sense of tactile and haptic feedback [1]. Okamura demonstrated a negative consequence of this by showing that forces exerted during robotic sutures significantly exceed that of hand sutures [2]. This excess in force can lead to a variety of complications including tissue crushing, which has been shown to be a clinically relevant problem [3]. Sie et al. proposed tissue-aware grasping as a solution for tissue crush injury, which may obviate the need for tactile and haptic feedback altogether [4]. By coupling online tissue identification with tissue-specific thresholds for crush injury, the surgical robot can warn of imminent tissue crushing or potentially prevent it. Sie et al. provided relevant work in this area by validating an approach for online tissue identification within the first 0.3 s of a grasp. This work was done with a modified manual laparoscopic Babcock grasper; this is a specialized instrument not commonly used in surgery. We herein aim to extend the results of Sie et al. to a much more common surgical tool: the da Vinci EndoWrist surgical instrument (Intuitive Surgical, Sunnyvale, CA). We demonstrate that tissue identification is possible using existing robotic tools without additional sensors or modifications to the tool tip, by using only motor torque and position data at the proximal end of the tool.

Design of a Portable Dynamic Calibration Instrument for daVinci Si Tools

“…The overall set-up utilized back-end sensors from the previously described da Vinci tool driver detailed in [4] referred to here as the Smart Tool ( Fig. 1) with a da Vinci Maryland Grasper (Intuitive Surgical, Sunnyvale, CA).…”

“…A proposed solution by Sie et al to mitigate these surgical errors is to create tissue-aware graspers which can be incorporated into existing surgical robots, such as the da Vinci [3]. Stephens et al created a tissue-aware grasper using backend sensing on a da Vinci Si tool [4]. However, tissue identification can be further improved through properly understanding a dynamic da Vinci tool model.…”

Effects of Grasp Frequency on the Dynamics of a Robotic Surgical Grasper1

“…The robotic surgical grasper analyzed in this experiment was a modified da Vinci EndoWrist tool; the complete design is detailed in Ref. [2]. Grasping is actuated at the proximal end of the tool by two servomotors (Hitec-HS422) interfaced to the tool's spindles.…”

Decay of Tissue Mechanical Properties Over a 24-hr Period1

“…A detailed design of this "smart" tool is found in Ref. [4]. Force and position data are collected directly from sensors on the tool.…”