Ongoing projects

Machine Learning-based Adaptive Robot-assisted Training for Surgical Robotics

People involved: Elena De MomiNima Enayati

Funding source: Intuitive Surgical Technology research grant

Funding period: 2017

Statement of Work:

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.


People involved: Elena De Momi, Giancarlo Ferrigno,  Nima Enayati, Hirenkumar Nakawala, Jacopo Buzziblueprint-green

Funding source: RIA H2020-ICT-2016
Grant number:H2020-ICT-2016- 732515

Funding period: 2017 – 2020

University of the West of England (United Kingdom), Ethniko Kentro Erenvas kai Technologikis Anaptyxis (Greece), North Bristol National Health Service Trust (United Kingdom), University of Bristol (United Kingdom), Istituto Europeo di Tecnologia (Italy), Idiotiko Poliiatrio Orthopaidikis Chirourgikis Athlitikon Kakoseon kai Apokatastasis Etairia Periorismeni Efthinis (Greece), Cybernetix (France), Optinvent (France), Hypertech Innovations Limited (United Kingdom)
Robot-assisted minimally invasive surgery (RAMIS) offers many advantages when compared to traditional MIS, including improved vision, precision and dexterity. While the popularity of RAMIS is steadily increasing, the potential for improving patient outcomes and penetrating into many procedures is not fully realised, largely because of serious limitations in the current instrumentation, control and feedback to the surgeon. Specifically, restricted access, lack of force feedback, and use of rigid tools in confined spaces filled with organs pose challenges to full adoption. We aim to develop novel technology to overcome barriers to expansion of RAMIS to more procedures, focusing on real-world surgical scenarios of urology, vascular surgery, and soft tissue orthopaedic surgery. A team of highly experienced clinical, academic, and industrial partners will collaborate to develop: i) dexterous anthropomorphic instruments with minimal cognitive demand ii) a range of bespoke end-effectors with embedded surgical tools using additive manufacturing methods for rapid prototyping and testing utilizing a user-centred approach, iii) wearable multi-sensory master for tele-operation to optimise perception and action and iv) wearable smart glasses for augmented reality guidance of the surgeon based on real-time 3D reconstruction of the surgical field, utilising dynamic active constraints and restricting the instruments to safe regions. The demonstration platform will be based on commercial robotic manipulators enhanced with the SMARTsurg advanced hardware and software features. Testing will be performed on laboratory phantoms with surgeons to bring the technology closer to exploitation and to validate acceptance by clinicians. The study will benefit patients, surgeons and health providers, by promoting safety and ergonomics as well as reducing costs. Furthermore, there is a potential to improve complex remote handling procedures in other domains beyond RAMIS.


People involved: Elena De Momi, Giancarlo Ferrigno, Alberto FavaroMarco Vidotto, Sara El Hadji EDEN2020 logo
Funding source: RIA H2020-ICT-2015
Grant number:ICT-24-2015- 688279

Funding period: 2016 – 2020

Due to an aging population and the spiralling cost of brain disease in Europe and beyond, EDEN2020 aims to develop the gold standard for one-stop diagnosis and minimally invasive treatment in neurosurgery. Supported by a clear business case, it will exploit the unique track record of leading research institutions and key industrial players in the field of surgical robotics to overcome the current technological barriers that stand in the way of real clinical impact.


EDEN2020 will provide a step change in the modelling, planning and delivery of diagnostic sensors and therapies to the brain via flexible surgical access, with an initial focus on cancer therapy. It will engineer a family of steerable catheters for chronic disease management that can be robotically deployed and kept in situ for extended periods. The system will feature enhanced autonomy, surgeon cooperation, targeting proficiency and fault tolerance with a suite of technologies that are commensurate to the unique challenges of neurosurgery. Amongst these, the system will be able to sense and perceive intraoperative, continuously deforming, brain anatomy at unmatched accuracy, precision and update rates, and deploy a range of diagnostic optical sensors with the potential to revolutionise today’s approach to brain disease management. By modelling and predicting drug diffusion within the brain with unprecedented fidelity, EDEN2020 will contribute to the wider clinical challenge of extending and enhancing the quality of life of cancer patients–with the ability to plan therapies around delicate tissue structures and with unparalleled delivery accuracy.

EDEN2020 is strengthened by a significant industrial presence, which is embedded within the entire R&D process to enforce best practices and maximise translation and the exploitation of project outputs. As it aspires to impact the state of the art and consolidate the position of European industrial robotics, it will directly support the Europe 2020 Strategy.
For more information, please visit EDEN2020 website.

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