Oleari, Elettra; Leporini, Alice; Trojaniello, Diana; Sanna, Alberto; Capitanio, Umberto; Deho, Federico; Larcher, Alessandro; Montorsi, Francesco; Salonia, Andrea; Setti, Francesco; Muradore, Riccardo Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures Journal Article pp. 1-6, 2019, ISBN: 978-1-7281-2342-4. Abstract | Links | BibTeX | Tags: Artificial Intelligence, Autonomy, Cognitive control, Cognitive functions, Decision making, Laparoscopes, Laparoscopy, Laparoscopy, learning systems, machine learning, Medical robotics, multirobots teleoperated platform, Robotic surgery, Surgery, Surgical robots, Teleoperation Hernansanz, Albert; Martínez, ; Rovira, ; Casals, Alicia A physical/virtual platform for hysteroscopy training Conference Proceedings of the 9th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery, 2019. Abstract | Links | BibTeX | Tags: Computer Science, Endoscopy, Laparoscopy, Laparoscopy, Robot, Robotic Surgery, Robotic surgery, Surgery, Surgical robots, Training
2019
title = {Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures},
author = {Elettra Oleari and Alice Leporini and Diana Trojaniello and Alberto Sanna and Umberto Capitanio and Federico Deho and Alessandro Larcher and Francesco Montorsi and Andrea Salonia and Francesco Setti and Riccardo Muradore},
editor = {IEEE},
doi = {10.1109/ISMICT.2019.8743931},
isbn = {978-1-7281-2342-4},
year = {2019},
date = {2019-05-09},
pages = {1-6},
abstract = {Nowadays Minimally Invasive Surgery (MIS) is playing an increasingly major role in the clinical practice also thanks to a rapid evolution of the available medical technologies, especially surgical robotics. A new challenge in this respect is to equip robots with cognitive capabilities, in order to make them able to act autonomously and cooperate with human surgeons. In this paper we describe the methodological approach developed to comprehensively describe a specific surgical knowledge, to be transferred to a complex Artificial Intelligence (AI) integrating Perception, Cognitive and Planning modules. Starting from desk researches and a strict cooperation with expert surgeons, the surgical process is framed on a high-level perspective, which is then deepened into a granular model through a Surgical Process Modelling approach, so as to embed all of the needed information by the AI to properly work. The model is eventually completed adding the corresponding Process Risk Analysis. We present the results obtained with the application of the aforementioned methodology to a Laparoscopic Radical Nephrectomy (LRN) procedure and discuss on the next technical implementation of this model.},
keywords = {Artificial Intelligence, Autonomy, Cognitive control, Cognitive functions, Decision making, Laparoscopes, Laparoscopy, Laparoscopy, learning systems, machine learning, Medical robotics, multirobots teleoperated platform, Robotic surgery, Surgery, Surgical robots, Teleoperation},
pubstate = {published},
tppubtype = {article}
}
title = {A physical/virtual platform for hysteroscopy training},
author = {Albert Hernansanz and Martínez and Rovira and Alicia Casals},
editor = {CRAS 2019},
doi = {10.5281/zenodo.3373297},
year = {2019},
date = {2019-03-21},
booktitle = {Proceedings of the 9th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery},
abstract = {This work presents HysTrainer (HT), a training module for hysteroscopy, which is part of the generic endoscopic training platform EndoTrainer (ET). This platform merges both technologies, with the benefits of having a physical anatomic model and computer assistance for augmented reality and objective assessment. Further to the functions of a surgical trainer, EndoTrainer provides an integral education, training and evaluation platform.},
keywords = {Computer Science, Endoscopy, Laparoscopy, Laparoscopy, Robot, Robotic Surgery, Robotic surgery, Surgery, Surgical robots, Training},
pubstate = {published},
tppubtype = {conference}
}
Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures Journal Article pp. 1-6, 2019, ISBN: 978-1-7281-2342-4. A physical/virtual platform for hysteroscopy training Conference Proceedings of the 9th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery, 2019.
2019