RESUMEN
Magnetically actuated endoscopes are currently transitioning in to clinical use for procedures such as colonoscopy, presenting numerous benefits over their conventional counterparts. Intelligent and easy-to-use control strategies are an essential part of their clinical effectiveness due to the un-intuitive nature of magnetic field interaction. However, work on developing intelligent control for these devices has mainly been focused on general purpose endoscope navigation. In this work, we investigate the use of autonomous robotic control for magnetic colonoscope intervention via biopsy, another major component of clinical viability. We have developed control strategies with varying levels of robotic autonomy, including semi-autonomous routines for identifying and performing targeted biopsy, as well as random quadrant biopsy. We present and compare the performance of these approaches to magnetic endoscope biopsy against the use of a standard flexible endoscope on bench-top using a colonoscopy training simulator and silicone colon model. The semi-autonomous routines for targeted and random quadrant biopsy were shown to reduce user workload with comparable times to using a standard flexible endoscope.
RESUMEN
Autonomous robotic Ultrasound (US) scanning has been the subject of research for more than 2 decades. However, little work has been done to apply this concept into a minimally invasive setting, in which accurate force sensing is generally not available and robot kinematics are unreliable due to the tendon-driven, compliant robot structure. As a result, the adequate orientation of the probe towards the tissue surface remains unknown and the anatomy reconstructed from scan may become highly inaccurate. In this work we present solutions to both of these challenges: an attitude sensor fusion scheme for improved kinematic sensing and a visual, deep learning based algorithm to establish and maintain contact between the organ surface and the US probe. We further introduce a novel scheme to estimate and orient the probe perpendicular to the center line of a vascular structure. Our approach enables, for the first time, to autonomously scan across a non-planar surface and navigate along an anatomical structure with a robotically guided minimally invasive US probe. Our experiments on a vessel phantom with a convex surface confirm a significant improvement of the reconstructed curved vessel geometry, with our approach strongly reducing the mean positional error and variance. In the future, our approach could help identify vascular structures more effectively and help pave the way towards semi-autonomous assistance during partial hepatectomy and the potential to reduce procedure length and complication rates.
RESUMEN
Magnetically actuated robots have become increasingly popular in medical endoscopy over the past decade. Despite the significant improvements in autonomy and control methods, progress within the field of medical magnetic endoscopes has mainly been in the domain of enhanced navigation. Interventional tasks such as biopsy, polyp removal, and clip placement are a major procedural component of endoscopy. Little advancement has been done in this area due to the problem of adequately controlling and stabilizing magnetically actuated endoscopes for interventional tasks. In the present paper we discuss a novel model-based Linear Parameter Varying (LPV) control approach to provide stability during interventional maneuvers. This method linearizes the non-linear dynamic interaction between the external actuation system and the endoscope in a set of equilibria, associated to different distances between the magnetic source and the endoscope, and computes different controllers for each equilibrium. This approach provides the global stability of the overall system and robustness against external disturbances. The performance of the LPV approach is compared to an intelligent teleoperation control method (based on a Proportional Integral Derivative (PID) controller), on the Magnetic Flexible Endoscope (MFE) platform. Four biopsies in different regions of the colon and at two different system equilibria are performed. Both controllers are asked to stabilize the endoscope in the presence of external disturbances (i.e. the introduction of the biopsy forceps through the working channel of the endoscope). The experiments, performed in a benchtop colon simulator, show a maximum reduction of the mean orientation error of the endoscope of 45.8% with the LPV control compared to the PID controller.
RESUMEN
Background and study aims Colonoscopy is a technically challenging procedure that requires extensive training to minimize discomfort and avoid trauma due to its drive mechanism. Our academic team developed a magnetic flexible endoscope (MFE) actuated by magnetic coupling under supervisory robotic control to enable a front-pull maneuvering mechanism, with a motion controller user interface, to minimize colon wall stress and potentially reduce the learning curve. We aimed to evaluate this learning curve and understand the user experience. Methods Five novices (no endoscopy experience), five experienced endoscopists, and five experienced MFE users each performed 40 trials on a model colon using 1:1 block randomization between a pediatric colonoscope (PCF) and the MFE. Cecal intubation (CI) success, time to cecum, and user experience (NASA task load index) were measured. Learning curves were determined by the number of trials needed to reach minimum and average proficiency-defined as the slowest average CI time by an experienced user and the average CI time by all experienced users, respectively. Results MFE minimum proficiency was achieved by all five novices (median 3.92 trials) and five experienced endoscopists (median 2.65 trials). MFE average proficiency was achieved by four novices (median 14.21 trials) and four experienced endoscopists (median 7.00 trials). PCF minimum and average proficiency levels were achieved by only one novice. Novices' perceived workload with the MFE significantly improved after obtaining minimum proficiency. Conclusions The MFE has a short learning curve for users with no prior experience-requiring relatively few attempts to reach proficiency and at a reduced perceived workload.
RESUMEN
Early diagnosis of colorectal cancer significantly improves survival. However, over half of cases are diagnosed late due to demand exceeding the capacity for colonoscopy - the "gold standard" for screening. Colonoscopy is limited by the outdated design of conventional endoscopes, associated with high complexity of use, cost and pain. Magnetic endoscopes represent a promising alternative, overcoming drawbacks of pain and cost, but struggle to reach the translational stage as magnetic manipulation is complex and unintuitive. In this work, we use machine vision to develop intelligent and autonomous control of a magnetic endoscope, for the first time enabling non-expert users to effectively perform magnetic colonoscopy in-vivo. We combine the use of robotics, computer vision and advanced control to offer an intuitive and effective endoscopic system. Moreover, we define the characteristics required to achieve autonomy in robotic endoscopy. The paradigm described here can be adopted in a variety of applications where navigation in unstructured environments is required, such as catheters, pancreatic endoscopy, bronchoscopy, and gastroscopy. This work brings alternative endoscopic technologies closer to the translational stage, increasing availability of early-stage cancer treatments.
RESUMEN
In this paper, explicit model predictive control is applied in conjunction with nonlinear optimisation to a magnetically actuated flexible endoscope for the first time. The approach is aimed at computing the motion of the external permanent magnet, given the desired forces and torques. The strategy described here takes advantage of the nonlinear nature of the magnetic actuation and explicitly considers the workspace boundaries, as well as the actuation constraints. Initially, a simplified dynamic model of the tethered capsule, based on the Euler-Lagrange equations is developed. Subsequently, the explicit model predictive control is described and a novel approach for the external magnet positioning, based on a single step, nonlinear optimisation routine, is proposed. Finally, the strategy is implemented on the experimental platform, where bench-top trials are performed on a realistic colon phantom, showing the effectiveness of the technique. The work presented here constitutes an initial exploration for model-based control techniques applied to magnetically manipulated payloads, the techniques described here may be applied to a wide range of devices, including flexible endoscopes and wireless capsules. To our knowledge, this is the first example of advanced closed loop control of magnetic capsules.
