Mission: 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 Surgical Data Science Initiative was founded to advance the emerging field of Surgical Data Science by international collaboration.
Previous events: The 1st workshop on Surgical Data Science was inspired by current open space and think tank formats and was organized in June 2016 in Heidelberg. Key results of this first workshop were a common definition of the field of surgical data science (see Fig. 1) as well as two white papers [1,2] that identify, present and discuss key initiatives, potential standards, new results, and challenges in the context of surgical data data.
Upcoming events: The second edition of the workshop will take place on June 17th 2019 in Rennes, France. You will soon be able to register to the event.
 Lena Maier-Hein, Swaroop S. Vedula, Stefanie Speidel, Nassir Navab, Ron Kikinis, Adrian Park, Matthias Eisenmann, Hubertus Feussner, Germain Forestier, Stamatia Giannarou, Makoto Hashizume, Darko Katic, Hannes Kenngott, Michael Kranzfelder, Anand Malpani, Keno März, Thomas Neumuth, Nicolas Padoy, Carla Pugh, Nicolai Schoch, Danail Stoyanov, Russell Taylor, Martin Wagner, Gregory D. Hager and Pierre Jannin. Surgical data science for next-generation interventions. Nature Biomedical Engineering 1(9), 691 (2017). DOI 10.1038/s41551-017-0132-7
 Lena Maier-Hein, Matthias Eisenmann, Carolin Feldmann, Hubertus Feussner, Germain Forestier, Stamatia Giannarou, Bernard Gibaud, Gregory D. Hager, Makoto Hashizume, Darko Katic, Hannes Kenngott, Ron Kikinis, Michael Kranzfelder, Anand Malpani, Keno März, Beat Müuller-Stich, Nassir Navab, Thomas Neumuth, Nicolas Padoy, Adrian Park, Carla Pugh, Nicolai Schoch, Danail Stoyanov, Russell Taylor, Martin Wagner, S. Swaroop Vedula, Pierre Jannin, Stefanie Speidel. Surgical data science: A consensus perspective, arXiv:1806.03184 2018
Fig. 1: Surgical data science aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis and modelling of data. It encompasses all clinical disciplines in which patient care requires intervention to manipulate anatomical structures with a diagnostic, prognostic or therapeutic goal […]. Data may pertain to any part of the patient-care process […] and are analysed in the context of generic domain-specific knowledge derived from existing evidence, clinical guidelines, current practice patterns, caregiver experience and patient preferences. […] Improvement may result from understanding processes and strategies, predicting events and clinical outcome, assisting physicians in decision-making and planning execution, optimizing the ergonomics of systems, controlling devices before, during and after treatment, and from advances in prevention, training, simulation and assessment. (Maier-Hein et al., Nature Biomedical Engineering 2018, DOI 10.1038/s41551-017-0132-7) .