Surgical Endoscopy

, Volume 32, Issue 6, pp 2958–2967 | Cite as

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

  • 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-StichEmail author
Dynamic Manuscript



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.


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.


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.


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.


Augmented reality Mobile device Image visualization Visual assistance 



The current study was conducted within the setting of the Research Training Group 1126 (“Development of New Computer-Based Methods for the Future Workplace in Surgery”) and the Collaborative Research Center 125 (“Cognition Guided Surgery”); both were funded by the German Research Foundation.

Compliance with ethical standards


Drs. 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, and Beat Peter Müller-Stich have no conflicts of interest or financial ties to disclose.

Supplementary material

Supplementary material 1 (MP4 29711 KB)


  1. 1.
    Bellini HC, Sugiyama M, Shin M, Alam S, Takayama D (2016) Virtual & augmented reality understanding the race for the next computing platform, Jan 13, 2016 edn. The Goldman Sachs Group, Inc., New York CityGoogle Scholar
  2. 2.
    Wurmb T, Balling H, Frühwald P, Keil T, Kredel M, Meffert R, Roewer N, Brederlau J (2009) Polytrauma management in a period of change: time analysis of new strategies for emergency room treatment. Der Unfallchirurg 112:390–399CrossRefPubMedGoogle Scholar
  3. 3.
    Wurmb TE, Quaisser C, Balling H, Kredel M, Muellenbach R, Kenn W, Roewer N, Brederlau J (2011) Whole-body multislice computed tomography (MSCT) improves trauma care in patients requiring surgery after multiple trauma. Emerg Med J 28:300–304CrossRefPubMedGoogle Scholar
  4. 4.
    Hilbert P, zur Nieden K, Hofmann GO, Hoeller I, Koch R, Stuttmann R (2007) New aspects in the emergency room management of critically injured patients: a multi-slice CT-oriented care algorithm. Injury 38:552–558CrossRefPubMedGoogle Scholar
  5. 5.
    Kay M, Santos J, Takane M (2011) mHealth: new horizons for health through mobile technologies. World Health Organization, GenevaGoogle Scholar
  6. 6.
    Mecheal PSD (2008) Towards the development of an mHealth strategy: a literature review. In: World Health Organization TMVPaTEIaCU. World Health Organization, GenevaGoogle Scholar
  7. 7.
    John S, Poh AC, Lim TC, Chan EH (2012) The iPad tablet computer for mobile on-call radiology diagnosis? Auditing discrepancy in CT and MRI reporting. J Digit Imaging 25:628–634CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Johnson PT, Zimmerman SL, Heath D, Eng J, Horton KM, Scott WW, Fishman EK (2012) The iPad as a mobile device for CT display and interpretation: diagnostic accuracy for identification of pulmonary embolism. Emerg Radiol 19:323–327CrossRefPubMedGoogle Scholar
  9. 9.
    Simpfendörfer T, Baumhauer M, Müller M, Gutt CN, Meinzer H-P, Rassweiler JJ, Guven S, Teber D (2011) Augmented reality visualization during laparoscopic radical prostatectomy. J Endourol 25:1841–1845CrossRefPubMedGoogle Scholar
  10. 10.
    Teber D, Simpfendörfer T, Guven S, Baumhauer M, Gözen AS, Rassweiler J (2010) In-vitro evaluation of a soft-tissue navigation system for laparoscopic prostatectomy. J Endourol 24:1487–1491CrossRefPubMedGoogle Scholar
  11. 11.
    Rassweiler JJ, Muller M, Fangerau M, Klein J, Goezen AS, Pereira P, Meinzer HP, Teber D (2012) iPad-assisted percutaneous access to the kidney using marker-based navigation: initial clinical experience. Eur Urol 61:628–631CrossRefPubMedGoogle Scholar
  12. 12.
    Baumhauer M, Simpfendörfer T, Müller-Stich BP, Teber D, Gutt CN, Rassweiler J, Meinzer HP, Wolf I (2008) Soft tissue navigation for laparoscopic partial nephrectomy. Int J Comput Assist Radiol Surg 3:307–314CrossRefGoogle Scholar
  13. 13.
    Muller M, Rassweiler MC, Klein J, Seitel A, Gondan M, Baumhauer M, Teber D, Rassweiler JJ, Meinzer HP, Maier-Hein L (2013) Mobile augmented reality for computer-assisted percutaneous nephrolithotomy. Int J Comput Assist Radiol Surg 8:663–675CrossRefPubMedGoogle Scholar
  14. 14.
    Kenngott HG, Wunscher JJ, Wagner M, Preukschas A, Wekerle AL, Neher P, Suwelack S, Speidel S, Nickel F, Oladokun D, Maier-Hein L, Dillmann R, Meinzer HP, Muller-Stich BP (2015) OpenHELP (Heidelberg laparoscopy phantom): development of an open-source surgical evaluation and training tool. Surg Endosc 29:3338–3347CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Nolden M, Zelzer S, Seitel A, Wald D, Muller M, Franz AM, Maleike D, Fangerau M, Baumhauer M, Maier-Hein L, Maier-Hein KH, Meinzer HP, Wolf I (2013) The medical imaging interaction toolkit: challenges and advances: 10 years of open-source development. Int J Comput Assist Radiol Surg 8:607–620CrossRefPubMedGoogle Scholar
  16. 16.
    Clutton RE, Blissitt KJ, Bradley AA, Camburn MA (1997) Comparison of three injectable anaesthetic techniques in pigs. Vet Rec 141:140–146CrossRefPubMedGoogle Scholar
  17. 17.
    Deng W, Li F, Wang M, Song Z (2013) Easy-to-use augmented reality neuronavigation using a wireless tablet PC. Stereotact Funct Neurosurg 92:17–24CrossRefPubMedGoogle Scholar
  18. 18.
    Kenngott H, Neuhaus J, Müller-Stich B, Wolf I, Vetter M, Meinzer H-P, Köninger J, Büchler M, Gutt C (2008) Development of a navigation system for minimally invasive esophagectomy. Surg Endosc 22:1858–1865CrossRefPubMedGoogle Scholar
  19. 19.
    Nickel F, Kenngott HG, Neuhaus J, Sommer CM, Gehrig T, Kolb A, Gondan M, Radeleff BA, Schaible A, Meinzer H-P (2013) Navigation system for minimally invasive esophagectomy: experimental study in a porcine model. Surg Endosc 27:3663–3670CrossRefPubMedGoogle Scholar
  20. 20.
    Cash DM, Miga MI, Glasgow SC, Dawant BM, Clements LW, Cao Z, Galloway RL, Chapman WC (2007) Concepts and preliminary data toward the realization of image-guided liver surgery. J Gastrointest Surg 11:844–859CrossRefPubMedGoogle Scholar
  21. 21.
    Carter TJ, Sermesant M, Cash DM, Barratt DC, Tanner C, Hawkes DJ (2005) Application of soft tissue modelling to image-guided surgery. Med Eng Phys 27:893–909CrossRefPubMedGoogle Scholar
  22. 22.
    Ukimura O, Gill IS (2008) Imaging-assisted endoscopic surgery: Cleveland clinic experience. J Endourol 22:803–810CrossRefPubMedGoogle Scholar
  23. 23.
    Kenngott HG, Wagner M, Gondan M, Nickel F, Nolden M, Fetzer A, Weitz J, Fischer L, Speidel S, Meinzer H-P, Böckler D, Büchler MW, Müller-Stich BP (2013) Real-time image guidance in laparoscopic liver surgery: first clinical experience with a guidance system based on intraoperative CT imaging. Surg Endosc 28:933–940CrossRefPubMedGoogle Scholar
  24. 24.
    Weidert S, Wang L, von der Heide A, Navab N, Euler E (2012) Intraoperative augmented reality visualization. Current state of development and initial experiences with the CamC. Der Unfallchirurg 115:209–213CrossRefPubMedGoogle Scholar
  25. 25.
    Shekhar R, Dandekar O, Bhat V, Philip M, Lei P, Godinez C, Sutton E, George I, Kavic S, Mezrich R (2010) Live augmented reality: a new visualization method for laparoscopic surgery using continuous volumetric computed tomography. Surg Endosc 24:1976–1985CrossRefPubMedGoogle Scholar
  26. 26.
    Hirai N, Kosaka A, Kawamata T, Hori T, Iseki H (2005) Image-guided neurosurgery system integrating AR-based navigation and open-MRI monitoring. Comput Aided Surg 10:59–72CrossRefPubMedGoogle Scholar
  27. 27.
    Nicolau S, Soler L, Mutter D, Marescaux J (2011) Augmented reality in laparoscopic surgical oncology. Surg Oncol 20:189–201CrossRefPubMedGoogle Scholar
  28. 28.
    Baumhauer M, Feuerstein M, Meinzer H-P, Rassweiler J (2008) Navigation in endoscopic soft tissue surgery: perspectives and limitations. J Endourol 22:751–766CrossRefPubMedGoogle Scholar
  29. 29.
    Oizumi H, Kato H, Watarai H, Sadahiro M (2013) Three-dimensional computed tomography image overlay facilitates thoracoscopic trocar placement. J Thorac Cardiovasc Surg 146:720–721CrossRefPubMedGoogle Scholar
  30. 30.
    Robinson JD (2012) The skeptical technophile: iPad review. J Digit Imaging 25:365–368CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Hannes Götz Kenngott
    • 1
  • Anas Amin Preukschas
    • 1
  • Martin Wagner
    • 1
  • Felix Nickel
    • 1
  • Michael Müller
    • 4
  • Nadine Bellemann
    • 2
  • Christian Stock
    • 3
  • Markus Fangerau
    • 2
  • Boris Radeleff
    • 2
  • Hans-Ulrich Kauczor
    • 2
  • Hans-Peter Meinzer
    • 4
  • Lena Maier-Hein
    • 4
  • Beat Peter Müller-Stich
    • 1
    Email author
  1. 1.Department of General, Visceral and Transplantation SurgeryHeidelberg UniversityHeidelbergGermany
  2. 2.Department of Diagnostic and Interventional RadiologyHeidelberg UniversityHeidelbergGermany
  3. 3.Institute for Medical Biometry and InformaticsHeidelberg UniversityHeidelbergGermany
  4. 4.Division of Medical and Biological InformaticsGerman Cancer Research CenterHeidelbergGermany

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