Automating Vascular Shunt Insertion with the dVRK Surgical Robot

Vascular shunt insertion is a fundamental surgical procedure used to temporarily restore blood flow to tissues. It is often performed in the field after major trauma. We formulate a problem of automated vascular shunt insertion and propose a pipeline to perform Automated Vascular Shunt Insertion (AVSI) using a da Vinci Research Kit. The pipeline uses a learned visual model to estimate the locus of the vessel rim, plans a grasp on the rim, and moves to grasp at that point. The first robot gripper then pulls the rim to stretch open the vessel with a dilation motion. The second robot gripper then proceeds to insert a shunt into the vessel phantom (a model of the blood vessel) with a chamfer tilt followed by a screw motion. Results suggest that AVSI achieves a high success rate even with tight tolerances and varying vessel orientations up to 30{\deg}. Supplementary material, dataset, videos, and visualizations can be found at https://sites.google.com/berkeley.edu/autolab-avsi

Superhuman Surgical Peg Transfer Using Depth-Sensing and Deep Recurrent Neural Networks

We consider the automation of the well-known peg-transfer task from the Fundamentals of Laparoscopic Surgery (FLS). While human surgeons teleoperate robots to perform this task with great dexterity, it remains challenging to automate. We present an approach that leverages emerging innovations in depth sensing, deep learning, and Peiper's method for computing inverse kinematics with time-minimized joint motion. We use the da Vinci Research Kit (dVRK) surgical robot with a Zivid depth sensor, and automate three variants of the peg-transfer task: unilateral, bilateral without handovers, and bilateral with handovers. We use 3D-printed fiducial markers with depth sensing and a deep recurrent neural network to improve the precision of the dVRK to less than 1 mm. We report experimental results for 1800 block transfer trials. Results suggest that the fully automated system can outperform an experienced human surgical resident, who performs far better than untrained humans, in terms of both speed and success rate. For the most difficult variant of peg transfer (with handovers) we compare the performance of the surgical resident with performance of the automated system over 120 trials for each. The experienced surgical resident achieves success rate 93.2 % with mean transfer time of 8.6 seconds. The automated system achieves success rate 94.1 % with mean transfer time of 8.1 seconds. To our knowledge this is the first fully automated system to achieve "superhuman" performance in both speed and success on peg transfer. Supplementary material is available at https://sites.google.com/view/surgicalpegtransfer

Challenges in surgical video annotation

Annotation of surgical video is important for establishing ground truth in surgical data science endeavors that involve computer vision. With the growth of the field over the last decade, several challenges have been identified in annotating spatial, temporal, and clinical elements of surgical video as well as challenges in selecting annotators. In reviewing current challenges, we provide suggestions on opportunities for improvement and possible next steps to enable translation of surgical data science efforts in surgical video analysis to clinical research and practice

Self-Supervised Learning for Interactive Perception of Surgical Thread for Autonomous Suture Tail-Shortening

Accurate 3D sensing of suturing thread is a challenging problem in automated surgical suturing because of the high state-space complexity, thinness and deformability of the thread, and possibility of occlusion by the grippers and tissue. In this work we present a method for tracking surgical thread in 3D which is robust to occlusions and complex thread configurations, and apply it to autonomously perform the surgical suture "tail-shortening" task: pulling thread through tissue until a desired "tail" length remains exposed. The method utilizes a learned 2D surgical thread detection network to segment suturing thread in RGB images. It then identifies the thread path in 2D and reconstructs the thread in 3D as a NURBS spline by triangulating the detections from two stereo cameras. Once a 3D thread model is initialized, the method tracks the thread across subsequent frames. Experiments suggest the method achieves a 1.33 pixel average reprojection error on challenging single-frame 3D thread reconstructions, and an 0.84 pixel average reprojection error on two tracking sequences. On the tail-shortening task, it accomplishes a 90% success rate across 20 trials. Supplemental materials are available at https://sites.google.com/berkeley.edu/autolab-surgical-thread/ .Comment: International Conference on Automation Science and Engineering (CASE) 2023, 7 page

European guidelines on management of restless legs syndrome: report of a joint task force by the European Federation of Neurological Societies, the European Neurological Society and the European Sleep Research Society

Background: Since the publication of the first European Federation of Neurological Societies (EFNS) guidelines in 2005 on the management of restless legs syndrome (RLS; also known as Willis-Ekbom disease), there have been major therapeutic advances in the field. Furthermore, the management of RLS is now a part of routine neurological practice in Europe. New drugs have also become available, and further randomized controlled trials have been undertaken. These guidelines were undertaken by the EFNS in collaboration with the European Neurological Society and the European Sleep Research Society. Objectives: To provide an evidence-based update of new treatments published since 2005 for the management of RLS. Methods: First, we determined what the objectives of management of primary and secondary RLS should be. We developed the search strategy and conducted a review of the scientific literature up to 31 December 2011 (print and electronic publications) for the drug classes and interventions employed in RLS treatment. Previous guidelines were consulted. All trials were analysed according to class of evidence, and recommendations made according to the 2004 EFNS criteria for rating. Recommendations: Level A recommendations can be made for rotigotine, ropinirole, pramipexole, gabapentin enacarbil, gabapentin and pregabalin, which are all considered effective for the short-term treatment for RLS. However, for the long-term treatment for RLS, rotigotine is considered effective, gabapentin enacarbil is probably effective, and ropinirole, pramipexole and gabapentin are considered possibly effective. Cabergoline has according to our criteria a level A recommendation, but the taskforce cannot recommend this drug because of its serious adverse events. © 2012 EFNS.Markku Partinen has received honoraria for consulting, advisory boards or lectures from Boehringer Ingelheim, Cephalon, GlaxoSmithKline, UCB Pharma, Leiras, Sanofi-Aventis, Orion, Servier, MSD, and Cephalon. He has received a grant from the Academy of Finland.Peer Reviewe