Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study
BackgroundAugmented reality (AR) systems are currently being explored by a broad spectrum of industries, mainly for improving point-of-care access to data and images. Especially in surgery and especially for timely decisions in emergency cases, a fast and comprehensive access to images at the patien...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
30 March 2018
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| In: |
Surgical endoscopy and other interventional techniques
Year: 2018, Volume: 32, Issue: 6, Pages: 2958-2967 |
| ISSN: | 1432-2218 |
| DOI: | 10.1007/s00464-018-6151-y |
| Online Access: | Verlag, Volltext: https://doi.org/10.1007/s00464-018-6151-y
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| Author Notes: | Hannes Götz Kenngott, Anas Amin Preukschas, Martin Wagner, Felix Nickel, Michael Müller, Nadine Bellemann, Christian Stock, Markus Fangerau, Boris Radeleff, Hans-Ulrich Kauczor, Hans-Peter Meinzer, Lena Maier-Hein, Beat Peter Müller-Stich |
| Summary: | BackgroundAugmented reality (AR) systems are currently being explored by a broad spectrum of industries, mainly for improving point-of-care access to data and images. Especially in surgery and especially for timely decisions in emergency cases, a fast and comprehensive access to images at the patient bedside is mandatory. Currently, imaging data are accessed at a distance from the patient both in time and space, i.e., at a specific workstation. Mobile technology and 3-dimensional (3D) visualization of radiological imaging data promise to overcome these restrictions by making bedside AR feasible.MethodsIn this project, AR was realized in a surgical setting by fusing a 3D-representation of structures of interest with live camera images on a tablet computer using marker-based registration. The intent of this study was to focus on a thorough evaluation of AR. Feasibility, robustness, and accuracy were thus evaluated consecutively in a phantom model and a porcine model. Additionally feasibility was evaluated in one male volunteer.ResultsIn the phantom model (n = 10), AR visualization was feasible in 84% of the visualization space with high accuracy (mean reprojection error ± standard deviation (SD): 2.8 ± 2.7 mm; 95th percentile = 6.7 mm). In a porcine model (n = 5), AR visualization was feasible in 79% with high accuracy (mean reprojection error ± SD: 3.5 ± 3.0 mm; 95th percentile = 9.5 mm). Furthermore, AR was successfully used and proved feasible within a male volunteer.ConclusionsMobile, real-time, and point-of-care AR for clinical purposes proved feasible, robust, and accurate in the phantom, animal, and single-trial human model shown in this study. Consequently, AR following similar implementation proved robust and accurate enough to be evaluated in clinical trials assessing accuracy, robustness in clinical reality, as well as integration into the clinical workflow. If these further studies prove successful, AR might revolutionize data access at patient bedside. |
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| Item Description: | Gesehen am 31.10.2019 |
| Physical Description: | Online Resource |
| ISSN: | 1432-2218 |
| DOI: | 10.1007/s00464-018-6151-y |