Multi-perspective workflow modeling for online surgical situation models.
BACKGROUND
Surgical workflow management is expected to enable situation-aware adaptation and intelligent systems behavior in an integrated operating room (OR). The overall aim is to unburden the surgeon and OR staff from both manual maintenance and information seeking tasks. A major step toward intelligent systems behavior is a stable classification of the surgical situation from multiple perspectives based on performed low-level tasks.


METHODS
The present work proposes a method for the classification of surgical situations based on multi-perspective workflow modeling. A model network that interconnects different types of surgical process models is described. Various aspects of a surgical situation description were considered: low-level tasks, high-level tasks, patient status, and the use of medical devices. A study with sixty neurosurgical interventions was conducted to evaluate the performance of our approach and its robustness against incomplete workflow recognition input.


RESULTS
A correct classification rate of over 90% was measured for high-level tasks and patient status. The device usage models for navigation and neurophysiology classified over 95% of the situations correctly, whereas the ultrasound usage was more difficult to predict. Overall, the classification rate decreased with an increasing level of input distortion.


CONCLUSIONS
Autonomous adaptation of medical devices and intelligent systems behavior do not currently depend solely on low-level tasks. Instead, they require a more general type of understanding of the surgical condition. The integration of various surgical process models in a network provided a comprehensive representation of the interventions and allowed for the generation of extensive situation descriptions.


CONCLUSIONS
Multi-perspective surgical workflow modeling and online situation models will be a significant pre-requisite for reliable and intelligent systems behavior. Hence, they will contribute to a cooperative OR environment.
University of Leipzig, Innovation Center Computer Assisted Surgery, Leipzig, Germany. Electronic address: stefan.franke@medizin.uni-leipzig.de.
Stefan
S
Franke
University Hospital Leipzig, Department of Neurosurgery, Germany; University of Leipzig, Innovation Center Computer Assisted Surgery, Leipzig, Germany.
Jürgen
J
Meixensberger
University of Leipzig, Innovation Center Computer Assisted Surgery, Leipzig, Germany.
Thomas
T
Neumuth
D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D025321:Surgery, Computer-Assisted / Q000193:education*; D057188:Workflow
Machine learning; Surgical workflow; Workflow modeling