Evaluation of a synergistically controlled semiautomatic trepanation system for neurosurgery

Abstract

One of the most common procedures in neurosurgery is the trepanation of the skull. In this paper, a synergistically controlled handheld tool for trepanation is introduced. This instrument is envisioned to reduce problems of dural tears and wide cutting gaps by combining a soft tissue preserving saw with an automatic regulation of the cutting depth. Since usability and safety of the semi-automatic handheld device are of utmost importance, the complex interaction between the user and the system has been analyzed extensively. Based on prospective usability evaluation the user interaction design and the corresponding user-interface were developed. The compliance with the relevant factors effectiveness, efficiency, error tolerance, learnability and user satisfaction was measured in user-centered experiments to evaluate the usability of the semiautomatic trepanation system. The results confirm the user interaction design of the semiautomatic trepanation system and the corresponding safety strategy. The system seems to integrate itself smoothly into the existing workflow and keeps the surgeon aware of the process.

Smart bioimpedance-controlled craniotomy: Concept and first experiments

“…As the minimal residual inaccuracies of CT image segmentation as well as registration and tracking are in the order of magnitude of 1–2 voxels, that is, 0.5–1 mm, 19 image-based robotic bone dissection has to preserve the final internal cortical layer adjacent to the dura and underlying soft tissue. 4,16 By contrast, Follmann et al 7 demonstrated that a dura mater displacement of up to 2 mm is possible without undesired soft tissue damage using the STS approach with a soft-tissue-preserving saw. However, preoperative CT data acquisition, intraoperative registration, and dynamic tracking are also required.…”

“…The STS device 7 has been adapted to our needs to realize a first prototype of a saw suitable for in-process bioimpedance measurement.…”

“…Different methods were evaluated for determining the local bone thickness or the detection of bone breakthrough. 7,8 However, they suffer from unsatisfactory robustness in the real context of use and partly necessitate additional intra- or preoperative steps related to information acquisition or registration in contrast to the conventional workflow. Therefore, we propose a novel approach using in-process bioimpedance measurement for cutting depth control without any further need for preoperative image acquisition, intraoperative registration or navigation.…”

Impact of image-guided surgery on surgeons' performance: a literature review

“…Examples are IGS systems which reduce the speed of the surgical instrument as soon as sensitive anatomical structures are approached too closely or disable it entirely (instrument disablement, ID-IGS) (Labadie and Fitzpatrick, 2011;Strauss et al, 2005;Strauss et al, 2007), or restrict the movement of the instrument attached to a robotic arm (movement restriction, MR-IGS) (Lim et al, 2016). Other systems automatically control the cutting depth based on navigation information while the surgeon freely moves the instrument on the skull surface (semiautomatic trepanation system, STS-IGS) (Follmann et al, 2010).…”

Smart Trepanation System: Preclinical Analysis of Safety, Efficiency, and User Satisfaction

“…Using preoperatively acquired data and position tracking, the skull thickness at each site can be determined automatically during the cutting process. 10 Therefore, a reliable algorithm and mechanism to perform real-time skull thickness determination and automatic depth control was developed. 6 The cutting accuracy of the STS was evaluated in another test series on two phantom skulls without any relevant overcutting/undercutting errors.…”