Robotics in neurointerventional surgery: a systematic review of the literature

BackgroundRobotically performed neurointerventional surgery has the potential to reduce occupational hazards to staff, perform intervention with greater precision, and could be a viable solution for teleoperated neurointerventional procedures.ObjectiveTo determine the indication, robotic systems used, efficacy, safety, and the degree of manual assistance required for robotically performed neurointervention.MethodsWe conducted a systematic review of the literature up to, and including, articles published on April 12, 2021. Medline, PubMed, Embase, and Cochrane register databases were searched using medical subject heading terms to identify reports of robotically performed neurointervention, including diagnostic cerebral angiography and carotid artery intervention.ResultsA total of 8 articles treating 81 patients were included. Only one case report used a robotic system for intracranial intervention, the remaining indications being cerebral angiography and carotid artery intervention. Only one study performed a comparison of robotic and manual procedures. Across all studies, the technical success rate was 96% and the clinical success rate was 100%. All cases required a degree of manual assistance. No studies had clearly defined patient selection criteria, reference standards, or index tests, preventing meaningful statistical analysis.ConclusionsGiven the clinical success, it is plausible that robotically performed neurointerventional procedures will eventually benefit patients and reduce occupational hazards for staff; however, there is no high-level efficacy and safety evidence to support this assertion. Limitations of current robotic systems and the challenges that must be overcome to realize the potential for remote teleoperated neurointervention require further investigation.

Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network

A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placing ginger seeds to achieve consistent ginger shoot orientation. To address the problem that existing ginger seeders have difficulty in automating seeding and ensuring consistent ginger shoot orientation, this study applies object detection techniques in deep learning to the detection of ginger and proposes a ginger recognition network based on YOLOv4-LITE, which, first, uses MobileNetv2 as the backbone network of the model and, second, adds coordinate attention to MobileNetv2 and uses Do-Conv convolution to replace part of the traditional convolution. After completing the prediction of ginger and ginger shoots, this paper determines ginger shoot orientation by calculating the relative positions of the largest ginger shoot and the ginger. The mean average precision, Params, and giga Flops of the proposed YOLOv4-LITE in the test set reached 98.73%, 47.99 M, and 8.74, respectively. The experimental results show that YOLOv4-LITE achieved ginger seed detection and ginger shoot orientation calculation, and that it provides a technical guarantee for automated ginger seeding.

Pressure Support versus Spontaneous Ventilation during Anesthetic Emergence—Effect on Postoperative Atelectasis: A Randomized Controlled Trial

Background Despite previous reports suggesting that pressure support ventilation facilitates weaning from mechanical ventilation in the intensive care unit, few studies have assessed its effects on recovery from anesthesia. The authors hypothesized that pressure support ventilation during emergence from anesthesia reduces postoperative atelectasis in patients undergoing laparoscopic surgery using the Trendelenburg position. Methods In this randomized controlled double-blinded trial, adult patients undergoing laparoscopic colectomy or robot-assisted prostatectomy were assigned to either the pressure support (n = 50) or the control group (n = 50). During emergence (from the end of surgery to extubation), pressure support ventilation was used in the pressure support group versus intermittent manual assistance in the control group. The primary outcome was the incidence of atelectasis diagnosed by lung ultrasonography at the postanesthesia care unit (PACU). The secondary outcomes were Pao2 at PACU and oxygen saturation measured by pulse oximetry less than 92% during 48 h postoperatively. Results Ninety-seven patients were included in the analysis. The duration of emergence was 9 min and 8 min in the pressure support and control groups, respectively. The incidence of atelectasis at PACU was lower in the pressure support group compared to that in the control group (pressure support vs. control, 16 of 48 [33%] vs. 28 of 49 [57%]; risk ratio, 0.58; 95% CI, 0.35 to 0.91; P = 0.024). In the PACU, Pao2 in the pressure support group was higher than that in the control group (92 ± 26 mmHg vs. 83 ± 13 mmHg; P = 0.034). The incidence of oxygen saturation measured by pulse oximetry less than 92% during 48 h postoperatively was not different between the groups (9 of 48 [19%] vs. 11 of 49 [22%]; P = 0.653). There were no adverse events related to the study protocol. Conclusions The incidence of postoperative atelectasis was lower in patients undergoing either laparoscopic colectomy or robot-assisted prostatectomy who received pressure support ventilation during emergence from general anesthesia compared to those receiving intermittent manual assistance. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Learning Breech Birth in an Upright Position Is Influenced by Preexisting Experience—A FRABAT Prospective Cohort Study

Background: Vaginal breech delivery is becoming an extinct art although national guidelines underline its safety and vaginal breech delivery in an upright position has been shown to be a safe birth mode option. In order to spread clinical knowledge and be able to implement vaginal breech delivery into obstetricians’ daily practice, we need to gather knowledge from facilities who teach specialized obstetrical management. Methods: We performed a prospective cohort study on 140 vaginal deliveries out of breech presentation solely-managed by seven newly-trained physicians and compared fetal outcome as well as rates of manual assistance in respect to preexisting experience. Results: Fetal morbidity rate measured with a modified PREMODA score was not significantly different in three sub-cohorts sorted by preexisting expertise levels of managing obstetricians (experience groups EG, EG0: 2, 5%; EG1: 3, 7.5%; EG2: 1, 1.7%; p = 0.357). Manual assistance rate was significantly higher in EG1 (low experience level in breech delivery and only in dorsal position) compared to EG0 and EG2 (EG1 28, 70%; EG0: 14, 25%; EG2: 21, 35%; p = 0.0008). Conclusions: Our study shows that vaginal breech delivery with newly-trained obstetricians is a safe option whether or not they have advanced preexisting expertise in breech delivery. These data should encourage implementing vaginal breech delivery in clinical routine.

Architecture of IoT and Challenges

Internet of things (IoT) architecture is an ecosystem of connected physical objects that are accessible through the internet. The ‘thing' in IoT could be a person with a heart monitor or an automobile with built-in-sensors (i.e., objects that have been assigned an IP address and have the ability to collect and transfer data over a network without manual assistance or intervention). The embedded technology in the objects helps them to interact with internal states or the external environment, which in turn affects the decisions taken. IoT world where all the devices and appliances are connected to a network and are used collaboratively to achieve complex tasks that require a high degree of intelligence, and IoT is an interaction between the physical and digital words using sensors and actuators. Furthermore, the IoT architecture may combine features and technologies suggested by various methodologies. IoT architecture is designed where the digital and real worlds are integrating and interacting constantly, and various technologies are merged together to form IoT.

A gantry robot system for cutting single Y-shaped welding grooves on plane workpieces

To guarantee the strength and precision of the final welding assemblies, it is necessary to cut welding grooves before welding thick workpieces. General methods to cut welding grooves on plane workpieces need much manual assistance, and some even need manual operation purely. Therefore, this paper proposed a robot system for cutting Y-shaped welding grooves with full automation. Flame cutting technology has been adopted, requiring no jig to fix workpieces, which also causes no direct vibration to robot structure. Vision-based sub-system firstly captures the edges to be cut, which are composed of continuous points, and a laser range finder (LRF) starts to obtain the thickness of the edges precisely. To convert these edges into the trajectory of flamer, Least Square Method and Hermite Interpolation are respectively utilized to fit lines and curves. Robot system subsequently computes the motion-related parameters according to the position of the edges and geometric parameters of the desired welding grooves. The inverse kinematics of this robot is solved by geometry methods, which decreases computation burden and saves much time compared with traditional algebra method. Another core novelty is that a velocity planning method combining optimization algorithm has been put forward, which, we think, is not only useful in this gantry robot but also benefits other motion axes with heavy load. This further reduces vibration. Finally, the simulation and experimental results both prove the feasibility of this system. To date, no available robots or machines tool can finish this process with full automation (to the best of our knowledge).

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

Automation is an inevitable trend in the development of tunnel shotcrete machinery. Tunnel environmental perception based on 3D LiDAR point cloud has become a research hotspot. Current researches about the detection of tunnel point clouds focus on the completed tunnel with a smooth surface. However, few people have researched the automatic detection method for steel arches installed on a complex rock surface. This paper presents a novel algorithm to extract tunnel steel arches. Firstly, we propose a refined function for calibrating the tunnel axis by minimizing the density variance of the projected point cloud. Secondly, we segment the rock surface from the tunnel point cloud by using the region-growing method with the parameters obtained by analyzing the tunnel section sequence. Finally, a Directed Edge Growing (DEG) method is proposed to detect steel arches on the rock surface in the tunnel. Our experiment in the highway tunnels under construction in Changsha (China) shows that the proposed algorithm can effectively extract the points of the edge of steel arches from 3D LiDAR point cloud of the tunnel without manual assistance. The results demonstrated that the proposed algorithm achieved 92.1% of precision, 89.1% of recall, and 90.6% of the F-score.

A Challenge-Based Approach to Body Weight�Supported Treadmill Training Poststroke: Protocol for a Randomized Controlled Trial (Preprint)

BACKGROUND Body weight support treadmill training protocols in conjunction with other modalities are commonly used to improve poststroke balance and walking function. However, typical body weight support paradigms tend to use consistently stable balance conditions, often with handrail support and or manual assistance. OBJECTIVE In this paper, we describe our study protocol, which involved 2 unique body weight support treadmill training paradigms of similar training intensity that integrated dynamic balance challenges to help improve ambulatory function post stroke. The first paradigm emphasized walking without any handrails or manual assistance, that is, hands-free walking, and served as the control group, whereas the second paradigm incorporated practicing 9 essential challenging mobility skills, akin to environmental barriers encountered during community ambulation along with hands-free walking (ie hands-free + challenge walking). METHODS We recruited individuals with chronic poststroke hemiparesis and randomized them to either group. Participants trained for 6 weeks on a self-driven, robotic treadmill interface that provided body weight support and a safe gait-training environment. We assessed participants at pre-, mid- and post 6 weeks of intervention-training, with a 6-month follow-up. We hypothesized greater walking improvements in the hands-free + challenge walking group following training because of increased practice opportunity of essential mobility skills along with hands-free walking. RESULTS We assessed 77 individuals with chronic hemiparesis, and enrolled and randomized 30 individuals poststroke for our study (hands-free group=19 and hands-free + challenge walking group=20) from June 2012 to January 2015. Data collection along with 6-month follow-up continued until January 2016. Our primary outcome measure is change in comfortable walking speed from pre to post intervention for each group. We will also assess feasibility, adherence, postintervention efficacy, and changes in various exploratory secondary outcome measures. Additionally, we will also assess participant responses to a study survey, conducted at the end of training week, to gauge each group's training experiences. CONCLUSIONS Our treadmill training paradigms, and study protocol represent advances in standardized approaches to selecting body weight support levels without the necessity for using handrails or manual assistance, while progressively providing dynamic challenges for improving poststroke ambulatory function during rehabilitation. CLINICALTRIAL ClinicalTrials.gov NCT02787759; https://clinicaltrials.gov/ct2/show/NCT02787759 (Archived by Webcite at http://www.webcitation.org/6yJZCrIea)