Surgical Data Science: A Consensus Perspective
Abstract
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.
Cite
@article{arxiv.1806.03184,
title = {Surgical Data Science: A Consensus Perspective},
author = {Lena Maier-Hein and Matthias Eisenmann and Carolin Feldmann and Hubertus Feussner and Germain Forestier and Stamatia Giannarou and Bernard Gibaud and Gregory D. Hager and Makoto Hashizume and Darko Katic and Hannes Kenngott and Ron Kikinis and Michael Kranzfelder and Anand Malpani and Keno März and Beat Müuller-Stich and Nassir Navab and Thomas Neumuth and Nicolas Padoy and Adrian Park and Carla Pugh and Nicolai Schoch and Danail Stoyanov and Russell Taylor and Martin Wagner and S. Swaroop Vedula and Pierre Jannin* and Stefanie Speidel*},
journal= {arXiv preprint arXiv:1806.03184},
year = {2018}
}
Comments
29 pages