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Register a new userOvercoming the Force Limitations of Magnetic Robotic Surgery: Impact-based Tetherless Suturing
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Added by
Onder Erin Dr.
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Magnetic robotics obviate the physical connections between the actuators and end effectors resulting in ultra-minimally invasive surgeries. Even though such a wireless actuation method is highly advantageous in medical applications, the trade-off between the applied force and miniature magnetic end effector dimensions has been one of the main challenges in practical applications in clinically relevant conditions. This trade-off is crucial for applications where in-tissue penetration is required (e.g., needle access, biopsy, and suturing). To increase the forces of such magnetic miniature end effectors to practically useful levels, we propose an impact-force-based suturing needle that is capable of penetrating into in-vitro and ex-vivo samples with 3-DoF planar freedom (planar positioning and in-plane orienting). The proposed optimized design is a custom-built 12 G needle that can generate 1.16 N penetration force which is 56 times stronger than its magnetic counterparts with the same size without such an impact force. By containing the fast-moving permanent magnet within the needle in a confined tubular structure, the movement of the overall needle remains slow and easily controllable. The achieved force is in the range of tissue penetration limits allowing the needle to be able to penetrate through tissues to follow a suturing method in a teleoperated fashion. We demonstrated in-vitro needle penetration into a bacon strip and successful suturing of a gauze mesh onto an agar gel mimicking a hernia repair procedure.
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Commercially available surgical-robot technology currently addresses many surgical scenarios for adult patients. This same technology cannot be used to the benefit of neonate patients given the considerably smaller workspace. Medically relevant procedures regarding neonate patients include minimally invasive surgery to repair congenital esophagus disorders, which entail the suturing of the fragile esophagus within the narrow neonate cavity. In this work, we explore the use of the SmartArm robotic system in a feasibility study using a neonate chest and esophagus model. We show that a medically inexperienced operator can perform two-throw knots inside the neonate chest model using the robotic system.
Force control is essential for medical robots when touching and contacting the patients body. To increase the stability and efficiency in force control, an Adaption Module could be used to adjust the parameters for different contact situations. We propose an adaptive controller with an Adaption Module which can produce control parameters based on force feedback and real-time stiffness detection. We develop methods for learning the optimal policies by value iteration and using the data generated from those policies to train the Adaptive Module. We test this controller on different zones of a persons arm. All the parameters used in practice are learned from data. The experiments show that the proposed adaptive controller can exert various target forces on different zones of the arm with fast convergence and good stability.
Autonomous robotic surgery has the potential to provide efficacy, safety, and consistency independent of individual surgeons skill and experience. Autonomous soft-tissue surgery in unstructured and deformable environments is especially challenging as it necessitates intricate imaging, tissue tracking and surgical planning techniques, as well as a precise execution via highly adaptable control strategies. In the laparoscopic setting, soft-tissue surgery is even more challenging due to the need for high maneuverability and repeatability under motion and vision constraints. We demonstrate the first robotic laparoscopic soft tissue surgery with a level of autonomy of 3 out of 5, which allows the operator to select among autonomously generated surgical plans while the robot executes a wide range of tasks independently. We also demonstrate the first in vivo autonomous robotic laparoscopic surgery via intestinal anastomosis on porcine models. We compared the criteria including needle placement corrections, suture spacing, suture bite size, completion time, lumen patency, and leak pressure between the developed system, manual laparoscopic surgery, and robot-assisted surgery (RAS). The ex vivo results indicate that our system outperforms expert surgeons and RAS techniques in terms of consistency and accuracy, and it leads to a remarkable anastomosis quality in living pigs. These results demonstrate that surgical robots exhibiting high levels of autonomy have the potential to improve consistency, patient outcomes, and access to a standard surgical technique.
Modeling of non-rigid object launching and manipulation is complex considering the wide range of dynamics affecting trajectory, many of which may be unknown. Using physics models can be inaccurate because they cannot account for unknown factors and the effects of the deformation of the object as it is launched; moreover, deriving force coefficients for these models is not possible without extensive experimental testing. Recently, advancements in data-powered artificial intelligence methods have allowed learnable models and systems to emerge. It is desirable to train a model for launch prediction on a robot, as deep neural networks can account for immeasurable dynamics. However, the inability to collect large amounts of experimental data decreases performance of deep neural networks. Through estimating force coefficients, the accepted physics models can be leveraged to produce adequate supplemental data to artificially increase the size of the training set, yielding improved neural networks. In this paper, we introduce a new framework for algorithmic estimation of force coefficients for non-rigid object launching, which can be generalized to other domains, in order to generate large datasets. We implement a novel training algorithm and objective for our deep neural network to accurately model launch trajectory of non-rigid objects and predict whether they will hit a series of targets. Our experimental results demonstrate the effectiveness of using simulated data from force coefficient estimation and shows the importance of simulated data for training an effective neural network.
Our previous work classified a taxonomy of suturing gestures during a vesicourethral anastomosis of robotic radical prostatectomy in association with tissue tears and patient outcomes. Herein, we train deep-learning based computer vision (CV) to automate the identification and classification of suturing gestures for needle driving attempts. Using two independent raters, we manually annotated live suturing video clips to label timepoints and gestures. Identification (2395 videos) and classification (511 videos) datasets were compiled to train CV models to produce two- and five-class label predictions, respectively. Networks were trained on inputs of raw RGB pixels as well as optical flow for each frame. Each model was trained on 80/20 train/test splits. In this study, all models were able to reliably predict either the presence of a gesture (identification, AUC: 0.88) as well as the type of gesture (classification, AUC: 0.87) at significantly above chance levels. For both gesture identification and classification datasets, we observed no effect of recurrent classification model choice (LSTM vs. convLSTM) on performance. Our results demonstrate CVs ability to recognize features that not only can identify the action of suturing but also distinguish between different classifications of suturing gestures. This demonstrates the potential to utilize deep learning CV towards future automation of surgical skill assessment.
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