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Surg Endosc. 2018 Jun;32(6):2958-2967. doi: 10.1007/s00464-018-6151-y. Epub 2018 Mar 30.

Mobile, real-time, and point-of-care augmented reality is robust, accurate, and feasible: a prospective pilot study.

Author information

1
Department of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
2
Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany.
3
Department of Diagnostic and Interventional Radiology, Heidelberg University, Heidelberg, Germany.
4
Institute for Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany.
5
Department of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany. beat.mueller@med.uni-heidelberg.de.

Abstract

BACKGROUND:

Augmented 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.

METHODS:

In 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.

RESULTS:

In 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.

CONCLUSIONS:

Mobile, 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.

KEYWORDS:

Augmented reality; Image visualization; Mobile device; Visual assistance

PMID:
29602988
DOI:
10.1007/s00464-018-6151-y
[Indexed for MEDLINE]

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