This doctoral thesis is among the first works of the young research area of Surgical Data Science. It explores different possibilities to recognize the surgical workflow of an ongoing surgery, quantitatively weigh diverse events happening during a surgery, and present intra-operative data based on these findings to the surgeon with high usability in mind.
The methods presented in this book can be used to establish a context-aware infrastructure, on which a wide variety of applications can be built in order to enable the often-cited “Operating Room of the Future”, which actively supports the surgeon through smart, assistive technologies.
The author studied computer science and earned his doctorate in 2018 at the Technical University of Munich with the Chair for Computer Aided Medical Procedures.
Ralf Stauder
Computer Science Convolutional Neural Networks Dynamic Time Warping Event Impact Factors Group Decision-Making Machine Learning Medical Informatics Operating Room of the Future Random Forests Surgical Data Science Surgical Usability Surgical Workflow Recognition TUM Technical University of Munich Technische Universität München