G6 - From Vision to Decision: Mapping a Presurgical Planning Session in VR to a Decision-Making Model

Introduction: : X-rays, CT scans, MRIs, and ultrasounds have long been used to reconstruct human anatomy in 3D[1]. Meanwhile, virtual reality (VR) has gained traction in both recreational and educational contexts, and the medical community now sees potential for clinical use[2]. Currently, surgeons rely on 2D image slices to mentally reconstruct 3D anatomy—a process requiring anatomical expertise and spatial reasoning, especially in fields like neurosurgery, orthopedics, and cardiac surgery, where precise spatial awareness is critical. VR is becoming a promising tool for presurgical planning, providing 3D reconstructions of human anatomy. Implementing VR as a viable surgical planning tool requires better understanding of surgeons’ decision-making processes in VR. After the surgical planning process, every surgeon has a mental model on how to approach a surgery. Existing literature evaluates surgeon decision-making before and after VR use[3], yet fails to capture the decision-making that occurs during the planning task. The rational decision-maker requires enough time to search for, and gather, necessary information [4]. They often face imperfect environmental conditions, and are constrained by time and available information. With VR, surgeons can spend less cognitive energy constructing a mental model of the surgical environment. Instead, they can spend more time making informed decisions surrounding the surgery itself. Our study aims to understand the impact VR has on the surgical planning process and how surgeons make decisions in VR.

Materials and

Methods: : A thoracic surgeon was recruited via email to participate in a study examining VR-based surgical planning. Five tumor cases were selected from standard imaging, segmented into anatomical components by support staff, and uploaded into the VR platform Enduvo. Using a headset and controllers, the surgeon reviewed each case in a dedicated VR space while vocalizing their thought process.

Six independent raters analyzed the recordings using thematic coding analysis. Recurring behaviors and decision-making actions were identified, and each rater time-stamped and labeled instances of these behaviors. Codes included short labels, definitions, and numeric identifiers for statistical analysis. Inter-rater reliability was assessed using a custom Fleiss’s kappa-based composite statistic, which accounted for overlapping code timing and adjusted for chance agreement.

To align observed behaviors with cognitive processes, rater-coded data were mapped onto a simplified Instance-Based Learning (sIBL) model (Fig. 1). Each rater’s codes were transformed into a matrix and decomposed via Singular Value Decomposition (SVD). Functional modes were extracted, averaged across raters, smoothed, and passed through a softmax function to represent decision-making processes over time. This framework enabled the interpretation of cognitive patterns behind surgical planning decisions beyond surface behaviors.

Results, Conclusions, and Discussions:: With VR-assisted surgical planning, the decision-making process is categorized into distinct phases based on the coding framework applied to surgeons’ interactions with the virtual models. These include: Environment, Recognize, Judgement, Choice, Memory, and Choice Threshold, defined in Table 1. Ultimately, each of these phases highlight how VR facilitates the planning of surgical strategies while also improving confidence in decision-making outcomes.

Using the determined thematic codes, they were then mapped to the Instance-Based Learning Tool (IBL) model. The Fleiss kappa scores were calculated among the raters for each recording. They were mapped to different phases based on how well they related to that phase and linked with the surgeon’s thoughts (Fig. 2). The IBL model was chosen as the decision-making model because the flow related well to the surgeon’s train of thought.

Instance Based Learning (IBL) is a learning theory that is linked to dynamic decision making, and it consists of five parts: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates []. The main tenet of this model is that the individual recognizes a situation they have encountered before, changes their mental judgement, and then edits the heuristic in their mind based on feedback to influence the choice of future decisions.

Based on the definition of IBL above, the various stages were first defined. These definitions were then used as a foundation to link the codes that the raters used to code the surgeon’s train of thought. Table 1 displays the classification of codes into the phases of the IBL model. In the future, we will assess how level of experience plays a role in surgeon’s decision making.

This study explored the impact of VR on pre-surgical planning. Through qualitative thematic analysis, patterns were identified in a surgeon’s decision-making approach, and this process was aligned to phases in the Instance-Based Learning model.

Acknowledgements and/or References (Optional):: [1] E. R. Zorzal et al., “Anatomy Studio: A tool for virtual dissection through augmented 3D reconstruction,” Computers & Graphics, vol. 85, pp. 74–84, Dec. 2019, doi: 10.1016/j.cag.2019.09.006. Available: https://www.sciencedirect.com/science/article/pii/S0097849319301578. [Accessed: May 01, 2025]
[2] M. M. T. E. Kouijzer, H. Kip, Y. H. A. Bouman, and S. M. Kelders, “Implementation of virtual reality in healthcare: a scoping review on the implementation process of virtual reality in various healthcare settings,” Implement Sci Commun, vol. 4, no. 1, p. 67, Jun. 2023, doi: 10.1186/s43058-023-00442-2. Available: https://doi.org/10.1186/s43058-023-00442-2. [Accessed: May 01, 2025]
[3] A. V. Reinschluessel, T. Muender, D. Salzmann, T. Döring, R. Malaka, and D. Weyhe, “Virtual Reality for Surgical Planning – Evaluation Based on Two Liver Tumor Resections,” Front. Surg., vol. 9, Feb. 2022, doi: 10.3389/fsurg.2022.821060. Available: https://www.frontiersin.orghttps://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2022.821060/full. [Accessed: May 01, 2025]
[4] W. Lancaster and J. Lancaster, “Rational Decision Making: Managing Uncertainty,” JONA: The Journal of Nursing Administration, vol. 12, no. 9, p. 23???28, Sep. 1982, doi: 10.1097/00005110-198209000-00007. Available: http://journals.lww.com/00005110-198209000-00007. [Accessed: May 01, 2025]