A. Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives

Over the last decades, visual endoscopy has become a gold standard for the detection and treatment of gastrointestinal cancers. However, mastering endoscopic procedures is complex and requires long hours of practice. In this context, simulation-based training represents a valuable opportunity for acquiring technical and cognitive skills, suiting the different trainees’ learning pace and limiting the risks for the patients. In this regard, the present contribution aims to present a critical and comprehensive review of the current technology for gastrointestinal (GI) endoscopy training, including both commercial products and platforms at a research stage. Not limited to it, the recent revolution played by the technological advancements in the fields of robotics, artificial intelligence, virtual/augmented reality, and computational tools on simulation-based learning is documented and discussed. Finally, considerations on the future trend of this application field are drawn, highlighting the impact of the most recent pandemic and the current demographic trends

Technical and Functional Validation of a Teleoperated Multirobots Platform for Minimally Invasive Surgery

Nowadays Robotic assisted Minimally Invasive Surgeries (R-MIS) are the elective procedures for treating highly accurate and scarcely invasive pathologies, thanks to their abil- ity to empower surgeons\u2019 dexterity and skills. The research on new Multi-Robots Surgery (MRS) platform is cardinal to the development of a new SARAS surgical robotic platform, which aims at carrying out autonomously the assistants tasks during R- MIS procedures. In this work, we will present the SARAS MRS platform validation protocol, framed in order to assess: (i) its technical performances in purely dexterity exercises, and (ii) its functional performances. The results obtained show a prototype able to put the users in the condition of accomplishing the tasks requested (both dexterity- and surgical-related), even with rea- sonably lower performances respect to the industrial standard. The main aspects on which further improvements are needed result to be the stability of the end effectors, the depth per- ception and the vision systems, to be enriched with dedicated virtual fixtures. The SARAS\u2019 aim is to reduce the main surgeon\u2019s workload through the automation of assistive tasks which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time

Computer assisted training for robotic surgery

description of a virtual reality simulator for robotic surger

A hFSM based cognitive control architecture for assistive task in R-MIS

This paper proposes a control architecture for surgical robotic assistive tasks in MIS using a hierarchical multi-level Finite State Machine (hFSM) as the cognitive control and a two-layered motion planner for the execution of the task. The hFSM models the operation starting from atomic actions to progressively build up more complex levels. The two-layer architecture of the motion planner merges the benefits of an offline geometric path construction method with those of online trajectory reconfiguration and reactive adaptation

Global/local motion planning based on Dynamic Trajectory Reconfiguration and Dynamical Systems for Autonomous Surgical Robots

This paper addresses the generation of collision-free trajectories for the autonomous execution of assistive tasks in Robotic Minimally Invasive Surgery (R-MIS). The proposed approach takes into account geometric constraints related to the desired task, like for example the direction to approach the final target and the presence of moving obstacles. The developed motion planner is structured as a two-layer architecture: a global level computes smooth spline-based trajectories that are continuously updated using virtual potential fields; a local level, exploiting Dynamical Systems based obstacle avoidance, ensures collision free connections among the spline control points. The proposed architecture is validated in a realistic surgical scenario

Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User Study

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFlexible endoscopes for colonoscopy present several limitations due to their inherent complexity, resulting in patient discomfort and lack of intuitiveness for clinicians. Robotic devices together with autonomous control represent a viable solution to reduce the workload of endoscopists and the training time while improving the overall procedure outcome. Prior works on autonomous endoscope control use heuristic policies that limit their generalisation to the unstructured and highly deformable colon environment and require frequent human intervention. This work proposes an image-based control of the endoscope using Deep Reinforcement Learning, called Deep Visuomotor Control (DVC), to exhibit adaptive behaviour in convoluted sections of the colon tract. DVC learns a mapping between the endoscopic images and the control signal of the endoscope. A first user study of 20 expert gastrointestinal endoscopists was carried out to compare their navigation performance with DVC policies using a realistic virtual simulator. The results indicate that DVC shows equivalent performance on several assessment parameters, being more safer. Moreover, a second user study with 20 novice participants was performed to demonstrate easier human supervision compared to a state-of-the-art heuristic control policy. Seamless supervision of colonoscopy procedures would enable interventionists to focus on the medical decision rather than on the control problem of the endoscope.Peer ReviewedPostprint (author's final draft

Physical simulator for colonoscopy: a modular design approach and validation

Simulators for gastrointestinal endoscopy offer the opportunity to train and assess clinicians’ skills in a low-risk and reliable environment. Physical simulators can enable a direct instrument-to-organ interaction not provided by virtual platforms. However, they present scarce visual realism and limited variability of the anatomical conditions. Herein, the authors present an innovative and low-cost methodology for designing and fabricating modular silicone colon simulators. The fabrication pipeline envisages parametric customization and development of 3D-printed molds for silicone pouring to obtain colon segments. The sizing of each colon segment is based on clinical data extracted by CT colonography images. Straight and curved segments are connected through silicone conjuncts to realize a customized and modular monolithic physical simulator. A 130 cm-long colon simulator prototype with assorted magnetically-connected polyps was fabricated and laid on a custom-made sensorized abdominal phantom. Content, face, and construct validity of the designed simulator were assessed by 17 endoscopists. In summary, this work demonstrated promising results for improving accessibility and flexibility of current colonoscopy physical simulators, paving the way for modular and personalized training programs.This work was supported by the ATLAS project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 813782