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Surgical data science for next-generation interventions

Nature Biomedical Engineering volume 1, pages 691–696 (2017)Cite this article

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Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.

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Acknowledgements

The authors thank the Transregional Collaborative Research Center (SFB/TRR) 125: Cognition-Guided Surgery, funded by the German Research Foundation (DFG), for sponsoring the workshop that served as basis for the manuscript (www.surgical-data-science.org/workshop2016). We also thank N. L. Rodas, M. A. Cypko and all other workshop participants for their valuable input during the workshop, and C. Feldmann for preparing the figures. We acknowledge the support of the European Research Council (ERC-2015-StG-37960), the US National Institutes of Health (NIH-R01EB01152407S1, NIH/NIBIB P41 EB015902, NIH/NCI U24CA180918, NIH/NIBIB P41 EB015898, NIH/NIBIB R01EB014955, NIH R01-DE025265), the US Department of Defense (DOD-W81XWH-13-1-0080), the Royal Society (UF140290) and the Link Foundation Fellowship in Advanced Simulation and Training.

Author information

Authors and Affiliations

  1. Division Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany

    Lena Maier-Hein, Matthias Eisenmann & Keno März

  2. The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, MD, 21218, USA

    Swaroop S. Vedula, Anand Malpani & Gregory D. Hager

  3. Division Translational Surgical Oncology, National Center for Tumor Diseases (NCT), 01307, Dresden, Germany

    Stefanie Speidel

  4. Computer Aided Medical Procedures, Technical University of Munich, 80333, Munich, Germany

    Nassir Navab

  5. Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21218, USA

    Nassir Navab, Anand Malpani, Russell Taylor & Gregory D. Hager

  6. Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, 02215, USA

    Ron Kikinis

  7. Department of Computer Science, University of Bremen, 28359, Bremen, Germany

    Ron Kikinis

  8. Fraunhofer MEVIS, 28359, Bremen, Germany

    Ron Kikinis

  9. Department of Surgery, Anne Arundel Health System, Annapolis, MD, 21401, USA

    Adrian Park

  10. Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA

    Adrian Park

  11. Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany

    Hubertus Feussner & Michael Kranzfelder

  12. Department of Computer Science, University of Haute-Alsace, 68093, Mulhouse, France

    Germain Forestier

  13. The Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, UK

    Stamatia Giannarou

  14. Department of Advanced Medical Initiatives, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan

    Makoto Hashizume

  15. Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technolgoy (KIT), 76131, Karlsruhe, Germany

    Darko Katic

  16. Department for General, Visceral and Transplant Surgery, Heidelberg University Hospital, 69120, Heidelberg, Germany

    Hannes Kenngott & Martin Wagner

  17. Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, 04103, Leipzig, Germany

    Thomas Neumuth

  18. ICube, University of Strasbourg, CNRS, IHU, 67081, Strasbourg, France

    Nicolas Padoy

  19. Department of Surgery, University of Wisconsin, Madison, WI, 53792, USA

    Carla Pugh

  20. Engineering Mathematics and Computing Lab (EMCL), IWR, Heidelberg University, 69120, Heidelberg, Germany

    Nicolai Schoch

  21. Centre for Medical Image Computing (CMIC) and Department of Computer Science, University College London, London, WC1E 6BT, UK

    Danail Stoyanov

  22. Université de Rennes 1, 35065, Rennes, France

    Pierre Jannin

  23. INSERM, 35043, Rennes, France

    Pierre Jannin

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  1. Lena Maier-Hein
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Corresponding authors

Correspondence to Lena Maier-Hein, Gregory D. Hager or Pierre Jannin.

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Competing interests

R.T. is a paid consultant to Galen Robotics, Inc. (owned by Johns Hopkins University; JHU) and owns equity in the company, and is also a co-inventor of technology licensed to Galen Robotics, Elekta, and Intuitive Surgical, for which R.T. has or may receive a portion of licensing fees. Although this Comment does not explicitly reference Galen Robotics or the licensed technology, JHU policy requires that these relationships be disclosed. These arrangements have been reviewed and approved by JHU in accordance with its conflict of interest policy. A.P. is on the scientific advisory board of Stryker Endoscopy (Stryker Corporation; Kalamazoo, Michigan, USA). D.S. is a paid part-time member of Touch Surgery, Kinosis Ltd. Although this Comment does not explicitly reference Touch Surgery technology, University College London (UCL) policy requires that these relationships be disclosed. These arrangements have been reviewed and approved by UCL in accordance with its conflict of interest policy.

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Maier-Hein, L., Vedula, S.S., Speidel, S. et al. Surgical data science for next-generation interventions. Nat Biomed Eng 1, 691–696 (2017). https://doi.org/10.1038/s41551-017-0132-7

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