Manipulating a flexible ureteroscope is difficult, due to its bendable body and hand–eye coordination problems, especially when exploring the lower pole of the kidney. Though robotic interventions have been adopted in various clinical scenarios, they are rarely used in ureteroscopy. This study proposes a teleoperation system consists of a soft robotic endoscope together with a Guidance Virtual Fixture (GVF) to help users explore the kidney’s lower pole. The soft robotic arm was a cable-driven, 3D-printed design with a helicoid structure. GVF was dynamically constructed using video streams from an endoscopic camera. With a haptic controller, GVF can provide haptic feedback to guide the users in following a trajectory. In the user study, participants were asked to follow trajectories when the soft robotic arm was in a retroflex posture. The results suggest that the GVF can reduce errors in the trajectory tracking tasks when the users receive the proper training and gain more experience. Based on the NASA Task Load Index questionnaires, most participants preferred having the GVF when manipulating the robotic arm. In conclusion, the results demonstrate the benefits and potential of using a robotic arm with a GVF. More research is needed to investigate the effectiveness of the GVFs and the robotic endoscope in ureteroscopic procedures.@en

Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video. The development of tools to automate certain tasks could alleviate the physical and mental load of doctors during interventions allowing them to focus on diagnosis and decision-making tasks. In this paper we present a synergic solution for intraluminal navigation consisting of a 3D printed endoscopic soft robot that can move safely inside luminal structures. Visual servoing based on Convolutional Neural Networks (CNNs) is used to achieve the autonomous navigation task. The CNN is trained with phantoms and in-vivo data to segment the lumen and a model-less approach is presented to control the movement in constrained environments. The proposed robot is validated in anatomical phantoms in different path configurations. We analyze the movement of the robot using different metrics such as task completion time smoothness error in the steady-state mean and maximum error. We show that our method is suitable to navigate safely in hollow environments and conditions which are different than the ones the network was originally trained on.@en

Background: In hip arthroplasties, surgeons rely on their experience to assess the stability and balance of hip tissues when fitting the implant to their patients. During the operation, surgeons use a modular, temporary set of implants to feel the tension in the surrounding soft tissues and adjust the implant configuration. This process is naturally subjective and therefore depends on the operator. Inexperienced surgeons undertaking hip arthroplasties are twice as likely to experience errors than their experienced colleagues, leading to dislocations, pain and discomfort for the patients. Methods: To address this issue, a new, 3DOF force measurement system was developed and integrated into the modular, trial implants that can quantify forces and movements intraoperatively in 3D. The prototypes were evaluated in three post-mortem human specimens (PMHSs), to provide surgeons with objective data to help determine the optimal implant fit and configuration. The devices comprise a deformable polymer material providing strain-based displacements measured with electromagnetic-based sensors and an inertial measurement unit (IMU) for motion data. Results: Device results show a relative accuracy of approx. 2% and a sensitivity of approx. 1%. PMHS results indicated that soft tissue forces on the hip joint peak in the order of ~100 N and trend with positions of the leg during range of motion (ROM) tests, although force patterns differ between each PMHS. Conclusion: By monitoring forces and force patterns of hip soft tissues, in combination with standardised ROM tests, the force patterns could shed a light on potential anomalies that can be addressed during surgery. Clinical and Translational Impact Statement: The development of an instrumented hip implant device for use during surgery knowledge will eventually allow us to develop a predictive model for soft tissue balancing, that can be used for pre- and intra-operative planning for each patient on a tailored and personalised basis. Ultimately, we hope that with this device, patients will benefit from a faster recovery, from a more-precisely fitted hip, and an improved quality of life.@en

Continuum robots such as robotic catheters are increasingly being used in minimally invasive surgery. Compliance contributes to enhanced safety during e.g. catheter insertion, however, estimation of contact force and location may help clinicians avoiding exerting excessive force. Ultimately this could lead to faster and safer interventions. Researchers proposed force sensors integrated in the catheter tip in the past. However, such sensors add extra complexity to the catheter design. Also, tip force sensors do not provide insights on forces that act along the catheter length. This paper proposes a data-driven approach for localizing contact forces that appear over the length of the catheter. The proposed approach consists of a collision detection method and a contact localization method. The framework only requires the measurement of the catheter's shape which can be done by an embedded multi-core Fiber Bragg Grating fiber. The method was validated experimentally with a 3D-printed continuum robot with an integrated multi-core fiber. A second contact localization method which is based on identifying the discontinuity in the measured curvature, is also implemented and compared with the proposed method. The static and dynamic experiments show a mean average localization error of 2.3 mm and 4.3 mm which correspond to respectively 3.3% and 6.1% of a 70 mm long flexible robot. These findings demonstrate that the proposed framework outperforms the previous methods and yield promising results. The contact state estimation algorithm can detect collisions in at most approximately 1.08s.

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