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Randomized Controlled Trial
. 2024 May;38(5):2483-2496.
doi: 10.1007/s00464-023-10615-8. Epub 2024 Mar 8.

Comparing a virtual reality head-mounted display to on-screen three-dimensional visualization and two-dimensional computed tomography data for training in decision making in hepatic surgery: a randomized controlled study

Anas Amin Preukschas  1   2 , Philipp Anthony Wise  1 , Lisa Bettscheider  1 , Micha Pfeiffer  3   4 , Martin Wagner  1 , Matthias Huber  3 , Mohammad Golriz  1 , Lars Fischer  5 , Arianeb Mehrabi  1 , Fabian Rössler  6 , Stefanie Speidel  4 , Thilo Hackert  1   2 , Beat Peter Müller-Stich  7 , Felix Nickel  1   2 , Hannes Götz Kenngott  8
Affiliations
Randomized Controlled Trial

Comparing a virtual reality head-mounted display to on-screen three-dimensional visualization and two-dimensional computed tomography data for training in decision making in hepatic surgery: a randomized controlled study

Anas Amin Preukschas et al. Surg Endosc. 2024 May.

Abstract

Objective: Evaluation of the benefits of a virtual reality (VR) environment with a head-mounted display (HMD) for decision-making in liver surgery.

Background: Training in liver surgery involves appraising radiologic images and considering the patient's clinical information. Accurate assessment of 2D-tomography images is complex and requires considerable experience, and often the images are divorced from the clinical information. We present a comprehensive and interactive tool for visualizing operation planning data in a VR environment using a head-mounted-display and compare it to 3D visualization and 2D-tomography.

Methods: Ninety medical students were randomized into three groups (1:1:1 ratio). All participants analyzed three liver surgery patient cases with increasing difficulty. The cases were analyzed using 2D-tomography data (group "2D"), a 3D visualization on a 2D display (group "3D") or within a VR environment (group "VR"). The VR environment was displayed using the "Oculus Rift ™" HMD technology. Participants answered 11 questions on anatomy, tumor involvement and surgical decision-making and 18 evaluative questions (Likert scale).

Results: Sum of correct answers were significantly higher in the 3D (7.1 ± 1.4, p < 0.001) and VR (7.1 ± 1.4, p < 0.001) groups than the 2D group (5.4 ± 1.4) while there was no difference between 3D and VR (p = 0.987). Times to answer in the 3D (6:44 ± 02:22 min, p < 0.001) and VR (6:24 ± 02:43 min, p < 0.001) groups were significantly faster than the 2D group (09:13 ± 03:10 min) while there was no difference between 3D and VR (p = 0.419). The VR environment was evaluated as most useful for identification of anatomic anomalies, risk and target structures and for the transfer of anatomical and pathological information to the intraoperative situation in the questionnaire.

Conclusions: A VR environment with 3D visualization using a HMD is useful as a surgical training tool to accurately and quickly determine liver anatomy and tumor involvement in surgery.

Keywords: Head mounted display; Hepatic surgery training; Three dimensional visualization; Virtual reality.

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Conflict of interest statement

Anas Amin Preukschas, Lisa Bettscheider, Martin Wagner, Matthias Huber, Arianeb Mehrabi, Stefanie Speidel, Thilo Hackert, Beat Peter Müller-Stich, Felix Nickel, Hannes Götz Kenngott received funding from the German Research Foundation (DFG) within the setting of the Collaborative Research Center 125: Cognition Guided Surgery. Philipp Anthony Wise, Micha Pfeiffer, Mohammad Golriz, Lars Fischer and Fabian Rössler have no conflicts of interest or financial ties to disclose.

Figures

Fig. 1
Fig. 1
Segmentation software examples: Medical Imaging Interaction Toolkit (below), ITK-Snap (top left), MeshMixer (top right)
Fig. 2
Fig. 2
Virtual reality workflow
Fig. 3
Fig. 3
Example of Virtual Reality environment from inside the Oculus Rift®
Fig. 4
Fig. 4
Experimental setup for both 2D (top right), 3D (top left), and VR (below) evaluation (2D shown). The laptop and VR headset were used for case evaluation. The digital tablet was used for answering the questionnaire options
Fig. 5
Fig. 5
Recruitment flowchart
Fig. 6
Fig. 6
Boxplot of average correct answers and time [min] to answer by visualization method, averaged across all patient cases. p-value annotation legend: ns not significant (p > 0.05), *1.00e−02 < p ≤ 5.00e−02, **1.00e−03 < p ≤ 1.00e−02, ***1.00e−04 < p ≤ 1.00e−03, ****p ≤ 1.00e−04, Diamond symbol signifies outliers
Fig. 7
Fig. 7
Boxplot graphs of average number of correct answers and time [min] to answer, split by study group and patient case. p-value annotation legend: ns not significant (p > 0.05), *1.00e−02 < p ≤ 5.00e−02, **1.00e−03 < p ≤ 1.00e−02, ***1.00e−04 < p ≤ 1.00e−03, ****p ≤ 1.00e−04, Diamond symbol signifies outliers
Fig. 8
Fig. 8
Answers to the evaluation questionnaire (Likert scale) by answer and group. p-value annotation legend: ns not significant (p > 0.05), *1.00e−02 < p ≤ 5.00e−02, **1.00e−03 < p ≤ 1.00e−02, ***1.00e−04 < p ≤ 1.00e−03, ****p ≤ 1.00e−04
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