Report
Surgical Data Science: Enabling Next-Generation Surgery
| العنوان: | Surgical Data Science: Enabling Next-Generation Surgery |
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| المؤلفون: | Maier-Hein, Lena, Vedula, Swaroop, Speidel, Stefanie, Navab, Nassir, Kikinis, Ron, Park, Adrian, Eisenmann, Matthias, Feussner, Hubertus, Forestier, Germain, Giannarou, Stamatia, Hashizume, Makoto, Katic, Darko, Kenngott, Hannes, Kranzfelder, Michael, Malpani, Anand, März, Keno, Neumuth, Thomas, Padoy, Nicolas, Pugh, Carla, Schoch, Nicolai, Stoyanov, Danail, Taylor, Russell, Wagner, Martin, Hager, Gregory D., Jannin, Pierre |
| المصدر: | Nature Biomedical Engineering 2017 |
| سنة النشر: | 2017 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computers and Society |
| الوصف: | 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. Comment: 10 pages, 2 figures, White paper corresponding to http://www.surgical-data-science.org/workshop2016 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1038/s41551-017-0132-7 |
| URL الوصول: | http://arxiv.org/abs/1701.06482 |
| رقم الانضمام: | edsarx.1701.06482 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1038/s41551-017-0132-7 |
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