Machine Learning-based Adaptive Robot-assisted Training for Surgical Robotics
Funding source: Intuitive Surgical Technology research grant
Funding period: 2017
The main hypothesis of this proposal is that training phases of a trainee can be recognized through performance measures and that a proportional physical guidance can measurably improve the outcome of the training procedure. The project comprises two principal aims:
Aim 1: Implementing trainee progress assessment through machine learning methods
The methodology for assessing surgical skills is gradually shifting from subjective scoring of an expert, which may be a variably biased opinion using vague and subjective criteria, towards a more quantitative analysis. This project aims at assessing various skills of a tele-operated surgical system’s operator through a Machine Learning (ML) powered agent. The assessment is in the context of training and thus the agent will be learning-phase-aware. For a set of predefined tasks, the ML agent will measure the performance of a trainee and produce an estimate of his/her progress with respect to the average learning curve of the specific task that can be used as an effective feedback to the trainee or in adapting accordingly the training program.
Aim 2: Design and evaluation of adaptive physical assistive methods in training
The second aim of the project is to investigate the effects of providing physical guidance in primary phases of tele-operated surgical training. This robot-assisted training guides the trainee through motion, force, torque, or vibration cues applied by the master device and is adapted to users’ progress modelled by the ML agent. Such an assistive method is hypothesized to facilitate the initial stages of training by preventing cognitive overload. These methods will be studied through multiple experiments using a tele-operation setup with statistically significant populations of subjects. The results of the study will reveal potential benefits or harms of motion guidance in skills training in terms of both immediate improvements and long-term retention.
Funding source: FP7-ICT-2009
Grant number:FP7-ICT-2009-6- 270460
Funding period: 2011 – 2015
The ACTIVE project exploits ICT and other engineering methods and technologies for the design and development of an integrated redundant robotic platform for neurosurgery. A light and agile redundant robotic cell with 20 degrees-of-freedom (DoFs) and an advanced processing unit for pre- and intra-operative control will operate both autonomously and cooperatively with surgical staff on the brain. As the patient will not be considered rigidly fixed to the operating table and/or to the robot, the system will push the boundaries of the state of the art in the fields of robotics and control for the accuracy and bandwidth required by the challenging and complex surgical scenario.
Two cooperating robots will interact with the brain that will deform for the tool contact, blood pressure, breathing and deliquoration. Human factors are considered by allowing easy interaction with the users through a novel haptic interface for tele-manipulation and by a collaborative control mode (“hands-on”). Active constraints will limit and direct tool tip position, force and speed preventing damage to eloquent areas, defined on realistic tissue models updated on-the-field through sensors information. The active constraints will be updated (displaced) in real time in response to the feedback from tool-tissue interactions and any additional constraints arising from a complex shared workspace. The overarching control architecture of ACTIVE will negotiate the requirements and references of the two slave robots.
The operative room represents the epitome of a dynamic and unstructured volatile environment, crowded with people and instruments. The workspace will thus be monitored by environmental cameras, and machine learning techniques will be used for the safe workspace sharing. Cognitive skills will help to identify the target location in the brain and constrain robotic motions by means of on-field observations.
For more information, please visit ACTIVE website.
Funding source: FP7-ICT-2011
Funding period: 2011 – 2013
Project European Robotic Surgery (EuRoSurge) is a Coordination and support action funded by the European Commission in the FP7-ICT-2011-7.This Coordination Action aims at developing a conceptual integration platform for Computer and Robot Aided Surgery (CRAS) research and manufacturing, based on the following actions:
- Identification of the key European players in surgical robotics, (both technological players, skilled end-users and EU funded projects);
- Identification of the key European players in cognitive sciences relevant to surgery;
- Creation of a glossary/ontology for cognitive surgical robotics;
Specification of a reference architecture for cognitive surgical robotics;
- Formulation of procedures for validation of surgical robots and their modules;
- Identification of non-technical roadblocks, e.g. patents, ethical and legal aspects.
For more information, please visit EUROSURGE website.
Funding period: 2008 – 2010
The ROBOCAST project focuses on robot assisted keyhole neurosurgery. This term refers to a brain surgery performed through a very small hole in the skull called burr hole. The reduced dimensions are the reason why it is called also “keyhole”.This surgery is carried out for several interventions, from endoscopy to biopsy and deep brain stimulation. Needles and catheters are inserted into the brain through the tiny hole for biopsy and therapy, including, among others the tasks of blood/fluid sampling, tissue biopsy, cryogenic and electrolytic ablation, brachytherapy, deep brain stimulation (DBS), diagnostic imaging, and a number of other minimally invasive surgical procedures. Related pathologies are tumours, hydrocephalus, dystonia, essential tremor, Parkinson’s Disease, Tourette Syndrome, clinical depression, phantom limb pain, cluster headache and epilepsy.
The ROBOCAST project outcome will be a system for the assistance of the surgeon during keyhole interventions on the brain. It will have a mechatronic part and an intelligence part. The mechatronic device will consist of a robot holding the instruments for the surgeon and inserting them in the brain with a smooth and precise controlled autonomous movement. The trajectory will be defined by the intelligence of the ROBOCAST system and will be approved by the surgeon, which is and remains the responsible of the outcome, before the insertion of the surgical instruments.
For more information, please visit ROBOCAST website.