Surgical Data Science: Enabling Next-Generation Surgery
Abstract
This paper introduces Surgical Data Science as an emerging scientific discipline. Key perspectives are based on discussions during an intensive two-day international interactive workshop that brought together leading researchers working in the related field of computer and robot assisted interventions. Our consensus opinion is that increasing access to large amounts of complex data, at scale, throughout the patient care process, complemented by advances in data science and machine learning techniques, has set the stage for a new generation of analytics that will support decision-making and quality improvement in interventional medicine. In this article, we provide a consensus definition for Surgical Data Science, identify associated challenges and opportunities and provide a roadmap for advancing the field.
Cite
@article{arxiv.1701.06482,
title = {Surgical Data Science: Enabling Next-Generation Surgery},
author = {Lena Maier-Hein and Swaroop Vedula and Stefanie Speidel and Nassir Navab and Ron Kikinis and Adrian Park and Matthias Eisenmann and Hubertus Feussner and Germain Forestier and Stamatia Giannarou and Makoto Hashizume and Darko Katic and Hannes Kenngott and Michael Kranzfelder and Anand Malpani and Keno März and Thomas Neumuth and Nicolas Padoy and Carla Pugh and Nicolai Schoch and Danail Stoyanov and Russell Taylor and Martin Wagner and Gregory D. Hager and Pierre Jannin},
journal= {arXiv preprint arXiv:1701.06482},
year = {2018}
}
Comments
10 pages, 2 figures, White paper corresponding to http://www.surgical-data-science.org/workshop2016