Phantom study on surgical performance in augmented reality laparoscopy

Abstract

Purpose: Only a few studies have evaluated Augmented Reality (AR) in in vivo simulations compared to traditional laparoscopy; further research is especially needed regarding the most effective AR visualization technique. This pilot study aims to determine, under controlled conditions on a 3D-printed phantom, whether an AR laparoscope improves surgical outcomes over conventional laparoscopy without augmentation.

Methods: We selected six surgical residents at a similar level of training and had them perform a laparoscopic task. The participants repeated the experiment three times, using different 3D phantoms and visualizations: Floating AR, Occlusion AR, and without any AR visualization (Control). Surgical performance was determined using objective measurements. Subjective measures, such as task load and potential application areas, were collected with questionnaires.

Results: Differences in operative time, total touching time, and SurgTLX scores showed no statistical significance ([Formula: see text]). However, when assessing the invasiveness of the simulated intervention, the comparison revealed a statistically significant difference ([Formula: see text]). Participants felt AR could be useful for various surgeries, especially for liver, sigmoid, and pancreatic resections (100%). Almost all participants agreed that AR could potentially lead to improved surgical parameters, such as operative time (83%), complication rate (83%), and identifying risk structures (83%).

Conclusion: According to our results, AR may have great potential in visceral surgery and based on the objective measures of the study, may improve surgeons' performance in terms of an atraumatic approach. In this pilot study, participants consistently took more time to complete the task, had more contact with the vascular tree, were significantly more invasive, and scored higher on the SurgTLX survey than with AR.

Keywords: Augmented reality; Instrument tracking; Intraoperative navigation; Laparoscopy; Phantom study; Visualization.

Fig. 1

The three selected pulmonary vessels used for 3D printing. The red markings define the target regions

Fig. 2

The workflow to create the phantoms: a we segmented blood vessels from patient CT scans in the medical image data processing suite ImFusion; b we embedded the 3D model of the vessel tree into a box; c a holder angled the box at 30°; d the resulting 3D-printed phantom was coated with graphite spray to provide electrical conductivity to the vascular tree; e we filled the phantom with cotton covered in colored wax, mimicking soft tissue

Fig. 3

The touch measuring setup of the phantom. a The participants used laparoscopic pliers and scissors to accomplish the procedure. b Diagram of the measurement setup. c The participant had to identify the code on the 3D-printed model by uncovering the white insulating tape with markings. d Physical appearance of the measuring setup

Fig. 4

The experimental setup: 1 an NDI tracking camera, 2 two screens respectively showing the endoscopic camera feed with and without augmentation 3 a laparoscopic trainer with the endoscope and surgical instruments connected to an oscilloscope. The participant (the person in the green shirt) performed the task while instructing an assistant (the person in the gray shirt) to move the endoscope

Fig. 5

Output of the three different AR visualization conditions. a In Floating AR, occlusions of the tools with the vessels are not handled correctly, as the virtual scene will always be superimposed on top of the video stream. b In Occlusion AR, the instruments may occlude the anatomy behind them through pixel-wise classification of the video stream. c In the Control condition, participants only see the unaltered output of the laparoscopic camera without any AR

Fig. 6

Boxplots of the objective performance measures according to the visualization mode

Fig. 7

Box plots of the SurgTLX results. The participants rated their task load after each run on a 7-point Likert scale (from 1 = “very low” to 7 = “a lot”)