Surgical Data Science -- from Concepts toward Clinical Translation
Abstract
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Cite
@article{arxiv.2011.02284,
title = {Surgical Data Science -- from Concepts toward Clinical Translation},
author = {Lena Maier-Hein and Matthias Eisenmann and Duygu Sarikaya and Keno März and Toby Collins and Anand Malpani and Johannes Fallert and Hubertus Feussner and Stamatia Giannarou and Pietro Mascagni and Hirenkumar Nakawala and Adrian Park and Carla Pugh and Danail Stoyanov and Swaroop S. Vedula and Kevin Cleary and Gabor Fichtinger and Germain Forestier and Bernard Gibaud and Teodor Grantcharov and Makoto Hashizume and Doreen Heckmann-Nötzel and Hannes G. Kenngott and Ron Kikinis and Lars Mündermann and Nassir Navab and Sinan Onogur and Raphael Sznitman and Russell H. Taylor and Minu D. Tizabi and Martin Wagner and Gregory D. Hager and Thomas Neumuth and Nicolas Padoy and Justin Collins and Ines Gockel and Jan Goedeke and Daniel A. Hashimoto and Luc Joyeux and Kyle Lam and Daniel R. Leff and Amin Madani and Hani J. Marcus and Ozanan Meireles and Alexander Seitel and Dogu Teber and Frank Ückert and Beat P. Müller-Stich and Pierre Jannin and Stefanie Speidel},
journal= {arXiv preprint arXiv:2011.02284},
year = {2021}
}