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Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten. Combining YAWL and DBNs for Surgical Phase Detection
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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
pp.1-15 |
| Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2016, Triberg-Nussbach> |
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| English |
| Conference Paper, Electronic Publication |
| Fraunhofer IOSB () |
Abstract
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).