The Case of a Knowledge-based Endoscopic Camera Guidance Robot
Buch, Englisch, 267 Seiten, Format (B × H): 148 mm x 210 mm, Gewicht: 371 g
ISBN: 978-3-658-14913-0
Verlag: Springer
Andreas Bihlmaier describes a novel method to model dynamic spatial relations by machine learning techniques. The method is applied to the task of representing the tacit knowledge of a trained camera assistant in minimally-invasive surgery. The model is then used for intraoperative control of a robot that autonomously positions the endoscope. Furthermore, a modular robotics platform is described, which forms the basis for this knowledge-based assistance system. Promising results from a complex phantom study are presented.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Chirurgie Minimalinvasive Chirurgie, Laserchirurgie, Laparoskopie
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
Weitere Infos & Material
Endoscope Robots and Automated Camera Guidance.- Knowledge-based Cognitive Systems.- Modular Research Platform for Robot-Assisted Surgery based on ROS.- Learning of Surgical Know-how by Models of Spatial Relations.- Intraoperative Camera Assistance.- Evaluation Study: TME in the Open Source Heidelberg Laparoscopic Phantom (OpenHELP).
