TY - JOUR
T1 - Surgical data science
T2 - A consensus perspective
AU - Maier-Hein, Lena
AU - Eisenmann, Matthias
AU - Feldmann, Carolin
AU - Feussner, Hubertus
AU - Forestier, Germain
AU - Giannarou, Stamatia
AU - Gibaud, Bernard
AU - Hager, Gregory D.
AU - Hashizume, Makoto
AU - Katic, Darko
AU - Kenngott, Hannes
AU - Kikinis, Ron
AU - Kranzfelder, Michael
AU - Malpani, Anand
AU - März, Keno
AU - Müller-Stich, Beat
AU - Navab, Nassir
AU - Neumuth, Thomas
AU - Padoy, Nicolas
AU - Park, Adrian
AU - Pugh, Carla
AU - Schoch, Nicolai
AU - Stoyanov, Danail
AU - Taylor, Russell
AU - Wagner, Martin
AU - Swaroop Vedula, S.
AU - Jannin, Pierre
AU - Speidel, Stefanie
N1 - Publisher Copyright:
Copyright © 2018, The Authors. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Surgical data science is a scientific discipline with the objective of improving the quality of interventional healthcare and its value through capturing, organization, analysis, and modeling of data. The goal of the 1st workshop on Surgical Data Science was to bring together researchers working on diverse topics in surgical data science in order to discuss existing challenges, potential standards and new research directions in the field. Inspired by current open space and think tank formats, it was organized in June 2016 in Heidelberg. While the first day of the workshop, which was dominated by interactive sessions, was open to the public, the second day was reserved for a board meeting on which the information gathered on the public day was processed by (1) discussing remaining open issues, (2) deriving a joint definition for surgical data science and (3) proposing potential strategies for advancing the field. This document summarizes the key findings.
AB - Surgical data science is a scientific discipline with the objective of improving the quality of interventional healthcare and its value through capturing, organization, analysis, and modeling of data. The goal of the 1st workshop on Surgical Data Science was to bring together researchers working on diverse topics in surgical data science in order to discuss existing challenges, potential standards and new research directions in the field. Inspired by current open space and think tank formats, it was organized in June 2016 in Heidelberg. While the first day of the workshop, which was dominated by interactive sessions, was open to the public, the second day was reserved for a board meeting on which the information gathered on the public day was processed by (1) discussing remaining open issues, (2) deriving a joint definition for surgical data science and (3) proposing potential strategies for advancing the field. This document summarizes the key findings.
KW - Biomedical Data Science
KW - Computer Aided Surgery
KW - Computer Assisted Interventions
KW - Robotics
KW - Surgical Data Science
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M3 - Article
AN - SCOPUS:85094495213
JO - Advances in Water Resources
JF - Advances in Water Resources
SN - 0309-1708
ER -