Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Combining YAWL and DBNs for Surgical Phase Detection

: Philipp, P.

Postprint urn:nbn:de:0011-n-4618225 (1.0 MByte PDF)
MD5 Fingerprint: adb09ae20a72ffd2e9126cbc5195e5b8
Created on: 24.8.2017

Beyerer, Jürgen (Ed.); Pak, Alexey (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2016. Proceedings : Triberg-Nussbach, July, 24 to 29, 2016
Karlsruhe: KIT Scientific Publishing, 2017 (Karlsruher Schriften zur Anthropomatik 33)
ISBN: 978-3-7315-0678-2
DOI: 10.5445/KSP/1000070009
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2016, Triberg-Nussbach>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

To provide assistance functions in context of surgical interventions,
the use of a surgical phase detection plays an important role. By
assessing the progress of an on-going surgery, a tailored (i.e., context sensitive) decision support for medical practitioners can be enabled. Subsequently, this provides opportunities to prevent errors, injuries, negligence or malpractices. In this work, a surgical phase detection, combining Yet Another Workflow Language (YAWL) with Dynamic Bayesian Networks
(DBNs) is proposed. Thereby, YAWL is used to model the relationship of
surgical phases; DBNs are used to allow for the detection of surgical phases
of interest. The approach is presented for the application example of a cholecystectomy (removal of the gallbladder).