Impact of Acoustic and Interactive Disruptive Factors during Robot-Assisted Surgery-A Virtual Surgical Training Model

Abstract

The use of virtual reality trainers for teaching minimally invasive surgical techniques has been established for a long time in conventional laparoscopy as well as robotic surgery. The aim of the present study was to evaluate the impact of reproducible disruptive factors on the surgeon's work. In a cross-sectional investigation, surgeons were tested with regard to the impact of different disruptive factors when doing exercises on a robotic-surgery simulator (Mimic Flex VR^TM). Additionally, we collected data about the participants' professional experience, gender, age, expertise in playing an instrument, and expertise in playing video games. The data were collected during DRUS 2019 (Symposium of the German Society for Robot-assisted Urology). Forty-two surgeons attending DRUS 2019 were asked to participate in a virtual robotic stress training unit. The surgeons worked in various specialties (visceral surgery, gynecology, and urology) and had different levels of expertise. The time taken to complete the exercise (TTCE), the final score (FSC), and blood loss (BL) were measured. In the basic exercise with an interactive disruption, TTCE was significantly longer (p < 0.01) and FSC significantly lower (p < 0.05). No significant difference in TTCE, FSC, or BL was noted in the advanced exercise with acoustic disruption. Performance during disruption was not dependent on the level of surgical experience, gender, age, expertise in playing an instrument, or playing video games. A positive correlation was registered between self-estimation and surgical experience. Interactive disruptions have a greater impact on the performance of a surgeon than acoustic ones. Disruption affects the performance of experienced as well as inexperienced surgeons. Disruption in daily surgery should be evaluated and minimized in the interest of the patient's safety.

Keywords: complications; disruption during surgery; laparoscopy; robotic surgery; surgical education; virtual reality trainer.

Figure 1

Mimic Flex VR^TM in use (obtained with the kind permission of Mimic Simulation).

Figure 2

(A) Set up for Peg Board and (B) for Energy Dissection (pictures from the simulator system of Mimic Flex VR^TM obtained with the kind permission of Mimic Simulation).

Figure 3

Test sequence.

Figure 4

Comparison of the final score (FSC) and the time to complete the exercise (TTCE) for Peg Board (A) and (B) and Energy Dissection (C) and (D) with and without disturbance. (E) Blood loss in Energy Dissection with and without disturbance. The Peg Board task yielded a higher final score (p < 0.05) and was completed in a shorter period of time (p < 0.01) without disturbance, whereas Energy Dissection revealed no significant difference between the attempts. Small circles and stars show outliers.

Figure 5

Correlation analysis between the number of minimally invasive operations as a surgeon and (A) ∆ Final Score in Peg Board, (B) ∆ Time to complete Peg Board, (C) Final Score Energy Dissection, (D) ∆ Time to complete Energy Dissection, and (E) ∆ Blood Loss Energy Dissection. No significant correlation was registered for any of the values (correlation coefficient: R, statistical significance: p, number: N).

Figure 6

Correlation analysis between the age of the participants and (A) ∆ Final Score in Peg Board, (B) ∆ Time to complete Peg Board, (C) Final Score Energy Dissection, (D) ∆ Time to complete Energy Dissection, and (E) ∆ Blood Loss Energy Dissection. No significant correlation was found for any of the values (correlation coefficient: R, statistical significance: p, number: N).

Figure 7

Correlation analysis between experience in practicing music, and (A) ∆ Final Score in Peg Board, (B) ∆ Time to complete Peg Board, (C) Final Score Energy Dissection, (D) ∆ Time to complete Energy Dissection, and (E) ∆ Blood Loss Energy Dissection. No significant correlation was found for any of the values (correlation coefficient: R, statistical significance: p, number: N).

Figure 8

Correlation analysis between experience in video games, and (A) ∆ Final Score in Peg Board (B) ∆ Time to complete Peg Board, (C) Final Score Energy Dissection, (D) ∆ Time to complete Energy Dissection, and (E) ∆ Blood Loss Energy Dissection. No significant correlation was registered for any of the values (correlation coefficient: R, statistical significance: p, number: N).

Figure 9

Correlation between self-estimated expertise as a surgeon and the number of minimally invasive operations performed as a surgeon. Due to a nonlinear correlation, a Loess fitting curve (85% of the values) was calculated. This curve approximates a logarithmic curve (see curved dotted line). If this curve is used as a basis and interindividual scattering is suspected, a range of 500–1500 (see vertical broken lines) operations may be used as a prerequisite for an expert (limit 80%, see horizontal broken line).