Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate cancer that involves complete or nerve sparing removal prostate tissue that contains cancer. After removal the bladder neck is successively sutured directly with the urethra. The procedure is called urethrovesical anastomosis and is one of the most dexterity demanding tasks during RALP. Two suturing instruments and a pair of needles are used in combination to perform a running stitch during urethrovesical anastomosis. While robotic instruments provide enhanced dexterity to perform the anastomosis, it is still highly challenging and difficult to learn. In this paper, we presents a vision-guided needle grasping method for automatically grasping the needle that has been inserted into the patient prior to anastomosis. We aim to automatically grasp the suturing needle in a position that avoids hand-offs and immediately enables the start of suturing. The full grasping process can be broken down into: a needle detection algorithm; an approach phase where the surgical tool moves closer to the needle based on visual feedback; and a grasping phase through path planning based on observed surgical practice. Our experimental results show examples of successful autonomous grasping that has the potential to simplify and decrease the operational time in RALP by assisting a small component of urethrovesical anastomosis.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.R obotic-assisted surgery is now well established in clinical practice and has become the gold-standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade, with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to repurpose decommissioned da Vinci surgical systems [Intuitive Surgical Inc. (ISI), California, USA] as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this article, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain and build upon this initiative.
Robotic-assisted partial nephrectomy is a surgical operation in which part of a kidney is removed typically because of the presence of a mass. Pre-operative and intraoperative imaging techniques are used to identify and outline the target mass, thus the margins of the resection area on the kidney surface. Drop-in ultrasound probes are used to acquire intraoperative images: the probe is inserted through a trocar port, grasped with a roboticassisted laparoscopic gripper and swiped on the kidney surface. Multiple swipes are performed to define the resection area. This is marked swipe by swipe using an electrocautery tool. During this procedure the probe often requires repositioning because of slippage from the target organ surface. Furthermore, the localization can be inaccurate when the target mass is in locations particularly hard to reach, and thus kidney repositioning could be required. A highly skilled surgeon is typically required to successfully perform this pre-operatory procedure. We propose a novel approach for the navigation of drop-in ultrasound probes: the use of pneumatically attachable flexible rails to enable swift, effortless, and accurate track-guided scanning of the kidney. The proposed system attaches on the kidney side surface with the use of a series of bio-inspired vacuum suckers. In this letter, the design of the proposed system and its use in robotic-assisted partial nephrectomy are presented for the first time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.