Fetoscopic Laser Coagulation (FLC) for Twin to Twin Transfusion Syndrome is a challenging intervention due to the working conditions: low quality images acquired from a 3 mm fetoscope inside a turbid liquid environment, local view of the placental surface, unstable surgical field and delicate tissue layers. FLC is based on locating, coagulating and reviewing anastomoses over the placenta's surface. The procedure demands the surgeons to generate a mental map of the placenta with the distribution of the anastomoses, maintaining, at the same time, precision in coagulation and protecting the placenta and amniotic sac from potential damages. This paper describes a teleoperated platform with a cognitive-based control that provides assistance to improve patient safety and surgery performance during fetoscope navigation, target re-location and coagulation processes. A comparative study between manual and teleoperated operation, executed in dry laboratory conditions, analyzes basic fetoscopic skills: fetoscope navigation and laser coagulation. Two exercises are proposed: first, fetoscope guidance and precise coagulation. Second, a resolved placenta (all anastomoses are indicated) to evaluate navigation, re-location and coagulation. The results are analyzed in terms of economy of movement, execution time, coagulation accuracy, amount of coagulated placental surface and risk of placenta puncture. In addition, new metrics, based on navigation and coagulation maps evaluate robotic performance. The results validate the developed platform, showing noticeable improvements in all the metrics.

Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and vessels. As navigation through lumens and vessels is quite complex, interest grows to establish autonomous navigation techniques for IP and EI for reaching the target area. Current research efforts are directed toward increasing the Level of Autonomy (LoA) during the navigation phase. One key ingredient for autonomous navigation is Motion Planning (MP) techniques. This paper provides an overview of MP techniques categorizing them based on LoA. Our analysis investigates advances for the different clinical scenarios. Through a systematic literature analysis using the PRISMA method, the study summarizes relevant works and investigates the clinical aim, LoA, adopted MP techniques, and validation types. We identify the limitations of the corresponding MP methods and provide directions to improve the robustness of the algorithms in dynamic intraluminal environments. MP for IP and EI can be classified into four subgroups: node, sampling, optimization, and learning-based techniques, with a notable rise in learning-based approaches in recent years. One of the review's contributions is the identification of the limiting factors in IP and EI robotic systems hindering higher levels of autonomous navigation. In the future, navigation is bound to become more autonomous, placing the clinician in a supervisory position to improve control precision and reduce workload.

Flexible Endoscopes (FEs) for colonoscopy present several limitations due to their inherent complexity, resulting in patient discomfort and lack of intuitiveness for clinicians. Robotic FEs with autonomous control represent a viable solution to reduce the workload of endoscopists and the training time while improving the procedure outcome. Prior works on autonomous endoscope FE 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 FE control using Deep Reinforcement Learning, called Deep Visuomotor Control (DVC), to exhibit adaptive behaviour in convoluted sections of the colon. DVC learns a mapping between the images and the FE control signal. A first user study of 20 expert gastrointestinal endoscopists was carried out to compare their navigation performance with DVC 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 users was performed to demonstrate easier human supervision compared to a state-of-the-art heuristic control policy. Seamless supervision of colonoscopy procedures would enable endoscopists to focus on the medical decision rather than on the control of FE.

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.

Robotic surgery has experienced a rapid progress in a diversity of interventions, especially in minimally invasive surgery (MIS). In this type of interventions the patient body is accessed by inserting especial instruments through small incisions. As these instruments are manipulated by robotic assistants, passive wrists are incorporated for free compliance with the port of entry. With this wrist configuration it is only possible to locate accurately an instrument tip if its port of entry or fulcrum point is known. A vision oriented method is proposed in this paper to estimate the position of this external point of incision. It is based on motion analysis in a sequence of images where geometric properties of projected features and 3D transformations of the instrument are related. In this work we show that it is possible to accurately estimate the position and orientation of this 3D point, as well as the orientation of the instrument with respect to the laparoscopic camera applying determined movements through the robotic assistant.