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Optimal Targeting Visualizations for Surgical Navigation of Iliosacral Screws

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12445)

Abstract

Surgical navigation can be used for complex orthopaedic procedures, such as iliosacral screw (ISS) fixations, to achieve accurate results. Although studies have documented the impact of navigation on surgical outcomes, no works to date have quantified the effects of how information regarding the surgical navigation scene is displayed to surgeons on conventional monitors. However, visualizing information in different ways can have a measurable effect on both accuracy and time required to perform the navigated task. We conducted a user study to investigate which visualization techniques helped non-expert users effectively navigate a surgical tool to screw targets. We proposed a series of 2D and 3D visualizations with varying representations of the targets and tool, and measured the time required and accuracy of each participant to perform the task. We found that a bullseye view and a ‘target-fixed with bullseye’ view allowed users to most efficiently complete simulated pelvic screw targeting, with mean accuracies of 0.47 mm and 0.73\(^{\circ }\) and 0.66 mm and 0.50\(^{\circ }\) respectively. Furthermore, our study found that 3D visualizations on their own are significantly less accurate and efficient, and that the orientation of the virtual surgical scene must match the user’s perspective of the physical scene to prevent unnecessary time mentally reconciling the two domains. These results can more systematically guide the development of visualizations in a surgical navigation system for screw insertions.

Keywords

  • Surgical navigation
  • Iliosacral screw insertion
  • Human computer interaction

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Correspondence to Prashant U. Pandey .

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Pandey, P.U., Guy, P., Lefaivre, K.A., Hodgson, A.J. (2020). Optimal Targeting Visualizations for Surgical Navigation of Iliosacral Screws. In: , et al. Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. CLIP ML-CDS 2020 2020. Lecture Notes in Computer Science(), vol 12445. Springer, Cham. https://doi.org/10.1007/978-3-030-60946-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-60946-7_1

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