Navigation Platform Research Articles

JUNO Project: Deployment and Validation of a Low-Cost Cloud-Based Robotic Platform for Reliable Smart Navigation and Natural Interaction with Humans in an Elderly Institution

This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered.

Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

This paper proposes the use of a small differential robot with two DC motors, controlled by an ESP32 microcon-troller, that implements the Rapidly Exploring Random Trees algorithm to navigate from an origin point to a destination point in an unknown but observable environment. The motivation behind this research is to explore the use of a low-cost, versatile and efficient robotic platform for autonomous navigation in complex environments. This work presents a practical and cost-effective solution that can be easily replicated and implemented in various scenarios such as search and rescue, surveillance, and industrial automation. The proposed robotic platform is equipped with a set of sensors and actuators that allow it to observe the environment, estimate its position, and move through it. The Rapidly Exploring Random Trees algorithm is implemented to generate a path from an origin to a destination point, avoiding obstacles and adjusting the robot’s motion accordingly. The implementation of this algorithm enables the robot to navigate through complex environments with high efficiency and reliability, making it a suitable solution for a wide range of applications. The results obtained through simulations and experiments show that the proposed robotic platform and algorithm achieve high performance and accuracy in autonomous navigation, even in complex environments.

COVID-19 Pandemic and the Use of Emergency Remote Teaching (ERT) Platforms: Lessons From a Nigerian University

Aim/Purpose: This study examines the use of the Emergency Remote Teaching (ERT) platform by undergraduates of the University of Ibadan, Nigeria, during the COVID-19 pandemic using the constructs of the UTAUT2 model. Five constructs of the UTAUT2 model were adopted to investigate the use of the ERT platform by undergraduates of the university. Background: The Coronavirus (COVID-19) outbreak disrupted academic activities in educational institutions, leading to an unprecedented school closure globally. In response to the pandemic, higher educational institutions adopted different initiatives aimed at ensuring the uninterrupted flow of their teaching and learning activities. However, there is little research on the use of ERT platforms by undergraduates in Nigerian universities. Methodology: The descriptive survey research design was adopted for the study. The multi-stage random sampling technique was used to select 334 undergraduates at the University of Ibadan, Nigeria, while a questionnaire was used to collect data from 271 students. Quantitative data were collected and analyzed using frequency counts, percentages, mean and standard deviation, Pearson Product Moment Correlation, and regression analysis. Contribution: The study contributes to understanding ERT use in the educational institutions of Nigeria – Africa’s most populous country. Furthermore, the study adds to the existing body of knowledge on how the UTAUT2 Model could explain the use of information technologies in different settings. Findings: Findings revealed that there was a positive significant relationship between habit, hedonic motivation, price value, and social influence on the use of ERT platforms by undergraduates. Hedonic motivation strongly predicted the use of ERT platforms by most undergraduates. Recommendations for Practitioners: As a provisional intervention in times of emergencies, the user interface, navigation, customization, and other aesthetic features of ERT platforms should be more appealing and enjoyable to ensure their optimum utilization by students. Recommendation for Researchers: More qualitative research is required on users’ satisfaction, concerns, and support systems for ERT platforms in educational institutions. Future studies could consider the use of ERT by students in different countries and contexts such as students participating in English as a Foreign Language (EFL) and the English for Speakers of other languages (ESOL) programs. Impact on Society: As society faces increased uncertainties of the next global pandemic, this article reiterates the crucial roles of information technology in enriching teaching and learning activities in educational institutions. Future Research: Future research should focus on how different technology theories and models could explain the use of ERT platforms at different educational institutions in other geographical settings and contexts.

Anticipating Spatial–Temporal Distribution of Regional Highway Traffic with Online Navigation Route Recommendation

Detailed anticipation of potential highway congestion is becoming more necessary, as increasing regional road traffic puts pressure on both highways and towns its passes through; tidal traffic during vacations and unsatisfactory town planning make the situation even worse. Remote sensing and on-site sensors can dynamically detect upcoming congestion, but they lack global and long-term perspectives. This paper proposes a demand-network approach that is based on online route recommendations to exploit its accuracy, coverage and timeliness. Specifically, a presumed optimal route is acquired for each prefecture pair by accessing an online navigation platform with its Application Programming Interface; time attributes are given to down-sampled route points to allocate traffic volume on that route to different hours; then different routes are weighted with the origin–destination traveler amount data from location-based services providers, resulting in fine-level prediction of the spatial–temporal distribution of traffic volume on highway network. Experiments with data in January 2020 show good consistency with empirical predictions of highway administrations, and they further reveal the importance of dealing with congestion hotspots outside big cities, for which we conclude that dynamic bypassing is a potential solution to be explored in further studies.

Novel Merging of CT and MRI to Allow for Safe Navigation into Kambin's Triangle for Percutaneous Lumbar Interbody Fusion-Initial Case Series Investigating Safety and Efficacy.

For percutaneous lumbar fusion (percLIF), magnetic resonance imaging and computed tomography are critical to defining surgical corridors. Currently, these scans are performed separately, and surgeons then use fluoroscopy or neuromonitoring to guide instruments through Kambin's triangle. However, anatomic variations and intraoperative positional changes are possible, meaning that safely accessing Kambin's triangle remains a challenge because nerveroot visualization without endoscopes has not been thoroughly described. To overcome the known challenges of percLIF and reduce the likelihood of iatrogenic injuries by showing real-time locations of neural and bony anatomy. The authors demonstrate an intraoperative navigational platform that applies nerve root segmentation and image fusion to assist with percLIF. Five patients from a single institution were included. Of the 5 patients, the mean age was 71 ± 8 years and 3 patients (60%) were female. One patient had general anesthesia while the remaining 4 patients underwent awake surgery with spinal anesthesia. The mean area for the L4-L5 Kambin's triangle was 76.1 ± 14.5 mm 2 . A case example is shown where the side of approach was based on the fact that Kambin's triangle was larger on one side compared with the other. The mean operative time was 170 ± 17 minutes, the mean blood loss was 32 ± 16 mL, and the mean hospital length of stay was 19.6 ± 8.3 hours. No patients developed postoperative complications. This case series demonstrates the successful and safe application of nerve segmentation using magnetic resonance imaging/computed tomography fusion to perform percLIF and provide positive patient outcomes.

The use of ‘ecommerce’ and ‘social commerce in emerging markets’: Omani female millennials’ experiences

Commercial social media (social commerce in emerging market) have increasingly been used in many emerging markets as an evolutionary form of ecommerce. While this trend has recently been examined from an e-vender's perspective, little is known about how customers, specifically female millennials, perceive of their shopping experiences with commercial social media in relation to ecommerce. Thus, this study explores the underlying motivations for using one e-retailing type over the other. We consider the social constructs of trust, customer service quality and web design in the study. Drawing data from 20 in-depth interviews with young Omani women, the study revealed that ecommerce is most preferred to commercial social media. It highlights the role of unethical marketing practices; unsystematic delivery costs; unprofessional customer service quality; underdeveloped local market and uneasiness of commercial social media in influencing the decision-making of the customers to shift to ecommerce. The study further points to the intersection between product's price, quality and information in building trust; the role of professionalism, responsiveness and empathy in enhancing customer service quality; the importance of product quality and variety in the local market; and the easy navigation and online payment of e-retailing platforms. The study bears implications on enhancing the features and services of commercial social media. It also calls for transforming the local market to meet the customers’ needs.

Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot

Interventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient’s vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons’ exposure to operational hazards including radiation. However, the absence of apt position control and force feedback remains a challenge. This study presents an isomorphic master–slave VISR for precise navigation of endovascular tools viz. catheters and guidewires. The master console aids operators in issuing manipulation commands and logs feedback from the force, rotation, and translation data. The slave manipulator uses the commands received from the master platform for actual tool navigation. However, precise master–slave position control and force feedback are precursors for optimal patient outcomes. This study utilized a fuzzy-PID controller for precise tool navigation and a neural network model for resistance force modulation with 50 mN precision. Furthermore, we evaluated the performance of using the learning-based models within our VISR and compared it with the performances from conventional methods. Results show that the models enhanced the proposed robotic system with better navigation precision, faster response speed, and improved force measurement capabilities.

Online health assessment method based on belief rule base with sliding time window considering input correlation and redundancy

This paper develops a new health assessment method based on belief rule base with sliding time window considering correlation and redundancy of input information (BRB-WCR). The new proposed method aims to solve four problems in health assessment of dynamic complex system: lack of system fault data, uncertainty of expert knowledge, correlation of characteristic and high real-time requirement of system. The traditional BRB model can address the first two problems while it assumes that the input characteristics are completely independent. In order to solve the two remaining problems, a new BRB-WCR model is developed, where the correlation and redundancy of characteristics are considered and the sliding time window for online training data is introduced to improve the real time performance of the model. Moreover, to address the uncertainty of expert knowledge, a new optimization model is developed to train the parameters of the BRB-WCR model. To illustrate the effectiveness of the developed method, a case study of inertial navigation platform is conducted.

Brain-Inspired Navigation Model Based on the Distribution of Polarized Sky-Light

This paper proposes a brain-inspired navigation model based on absolute heading for the autonomous navigation of unmanned platforms. The proposed model combined the sand ant’s strategy of acquiring absolute heading from the sky environment and the brain-inspired navigation system, which is closer to the navigation mechanism of migratory animals. Firstly, a brain-inspired grid cell network model and an absolute heading-based head-direction cell network model were constructed based on the continuous attractor network (CAN). Then, an absolute heading-based environmental vision template was constructed using the line scan intensity distribution curve, and the path integration error was corrected using the environmental vision template. Finally, a topological cognitive node was constructed according to the grid cell, the head direction cell, the environmental visual template, the absolute heading information, and the position information. Numerous topological nodes formed the absolute heading-based topological map. The model is a topological navigation method not limited to strict geometric space scale, and its position and absolute heading are decoupled. The experimental results showed that the proposed model is superior to the other methods in terms of the accuracy of visual template recognition, as well as the accuracy and topology consistency of the constructed environment topology map.

COMBINING REAL-TIME 3-D IMAGING AND AUGMENTED FLUOROSCOPY WITH ROBOTIC BRONCHOSCOPY FOR THE DIAGNOSIS OF PERIPHERAL LUNG NODULES

COMBINING REAL-TIME 3-D IMAGING AND AUGMENTED FLUOROSCOPY WITH ROBOTIC BRONCHOSCOPY FOR THE DIAGNOSIS OF PERIPHERAL LUNG NODULES

Queueing-Network Based Applications Under Worst-case Attacks

A variety of today's critical applications are based on queueing networks whose performance, mainly delay, depends on routing and resource allocation. These include computer networks (the Internet), load-balancers on cloud systems and vehicular traffic networks. These applications are vulnerable to malicious attacks which may include the dis-functioning of the network components by malwares, or other computer viruses. For computer networks there are many references (e.g., [1, 5]); for vehicular traffic networks see navigation platforms attacks (e.g., Waze [4]). Such attacks will increase the delay experienced in the network and degrade its performance. We aim at understanding what are the weak-points of such networks. That is, how can a sophisticated attacker cause the maximal damage to the network using minimal attacking power. Furthermore, we are interested in analyzing how flexible is the network in reacting to such attacks by re-routing its traffic, and whether such flexibility grants significant protection. An intriguing question, which we aim at addressing and which may affect network planning, is what will be the nature of an optimal attack: will it be concentrated at few nodes of the network, or would it be scattered over many regions (nodes). Prior works (e.g., [1, 3, 5]) that dealt with such worstcase attacks on distributed systems did not address queueing delays and their effects. To allow analytic treatment that will reveal the nature of these networks, we consider here a simplistic queueing model based on k queues which captures the main features of these networks: (i) Multi-commodity arrival flows, (ii) The ability of the network to migrate requests (users) from one route to another. Treatment of general structure networks is the subject of ongoing research which is based on this work. Our analysis reveals somewhat surprising properties: The nature of an optimal attack varies as a function of the system parameters, and may shift from fully concentrated to fully scattered in the extreme cases. This is in contrast to the (no queueing) model and results of [5] which asserted that optimal attacks are concentrated, even when the system can defend itself using requests migration. This suggests that queueing mechanisms and the consideration of queueing delays may cause scattering of optimal attacks.

Development and experimental analysis of a small integrated edge navigation sensor based on principle of circular arc array.

Signal, accuracy, and real-time performance of satellite, radar, and machine vision is a subject of concern in various complex agricultural environments. Therefore, the demand for a robust navigation sensor for indoor and vertical agricultural environment remains crucial, and it is a significant subject. In view of this, the relative edge pose detection method based on the ideal target band principle of the lateral center arc array, in this research, a small integrated arc array navigation sensor module based on adaptive detection arc technology, is developed, which costs about $100, autonomous edge navigation position, and attitude detection is realized in facility agriculture environment with continuous structured corridor or roadside features. In this research, a coupling method of reducing the radius of distance sensor arrangement, adjusting the unequal center angle, and increasing the detection distance is used to realize the miniaturization of the arc array arrangement. A semicircular modular rocket was designed to slide and adjust the center angle of the distance sensor, and the longitudinal installation position of the modularized sensor was adjusted by translating the circular arc of the detection; the convenient moving arrangement under different vehicle width and detection arc characteristics is realized. An adaptive construction method of detecting a circular arc based on self-calibrating detection distance of a distance sensor is proposed, which effectively reduces the difficulty of arranging the lateral central circular arc array; the fast construction of lateral detection arc is realized. In addition, in order to improve the accuracy and stability of the pose detection, the Mahalanobis distance algorithm and the standard Kalman filter are used to optimize the estimation of the ranging information and the relative pose of the edge. The experimental results show that the small integrated arc array navigation sensor module can independently construct photoelectric detection arcs with different characteristics to detect the position and attitude of the relative edge. When the road surface is concave and convex, the small integrated arc array navigation sensor module can still maintain the stable position and attitude detection of the relative edge for more than 30 s. In addition, when the walking speed of the autonomous navigation platform is 0.15 m/s to 0.35 m/s, the detection errors of lateral deviation and heading deviation relative to the road edge are less than 40 mm and 4.5°, respectively. The small integrated arc array navigation sensor module is less affected by the change of operating speed, and still has good accuracy and stability. The results show that the modularized edge navigation sensor has the advantages of fast and convenient use, high accuracy, and low cost; it can be applied to autonomous edge navigation control in greenhouse and plant and animal factories.

Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs.

Navigation safety is one of the primary operational requirements for unmanned surface vehicles (USVs) in a complex marine environment, mainly guaranteed by a reliable path planning system for collision avoidance. This work proposes a novel weighted sum multi-objective optimisation strategy for USV collision avoidance path planning in restricted waters. In particular, the coefficients of different objectives could be tuned to emphasise the most critical design consideration under varying navigation scenarios. Moreover, in addition to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), the terrain and weather constraints were also considered in the path planning system. The proposed USV collision avoidance path planning framework’s effectiveness was demonstrated through numerical simulations and hardware-in-the-loop (HIL) tests. The numerical simulation results indicate that the proposed method could avoid collision with dynamic and static obstacles, and it is also adaptive to different navigation restrictions and preferences. Moreover, a USV navigation platform was established by incorporating true Automatic Identification System (AIS) signals, and HIL tests were performed with real-time AIS data in a water channel in the Zhoushan archipelago. The results demonstrate that the proposed USV path planning strategy is applicable in restricted waters with complex terrains and weather constraints.

A novel clip-on device for electromagnetic tracking in endobronchial ultrasound bronchoscopy

Introduction The established method for assessment of mediastinal and hilar lymph nodes is endobronchial ultrasound bronchoscopy (EBUS) with needle aspirations. Previously, we presented an electromagnetic navigation platform for this purpose. There were several issues with the permanent electromagnetic tracking (EMT) sensor attachment on the tip of the experimental EBUS bronchoscope. The purpose was to develop a device for on-site attachment of the EMT sensor. Material and methods A clip-on EMT sensor attachment device was 3D-printed in Ultem™ and attached to an EBUS bronchoscope. A specially designed ultrasound probe calibration adapter was developed for on-site and quick probe calibration. Navigation accuracy was studied using a wire cross water phantom and clinical feasibility was tested in a healthy volunteer. Results The device attached to the EBUS bronchoscope increased its diameter from 6.9 mm to 9.5 mm. Average preclinical navigation accuracy was 3.9 mm after adapter calibration. The maneuvering of the bronchoscope examining a healthy volunteer was adequate without harming the respiratory epithelium, and the device stayed firmly attached. Conclusion Development, calibration and testing of a clip-on EMT sensor attachment device for EBUS bronchoscopy was successfully demonstrated. Acceptable accuracy results were obtained, and the device is ready to be tested in patient studies.

Digital Strategies to Accelerate Help-Seeking in Youth With Psychiatric Concerns in New York State.

BackgroundMental illness in transition age youth is common and treatment initiation is often delayed. Youth overwhelmingly report utilizing the Internet to gather information while psychiatric symptoms emerge, however, most are not yet ready to receive a referral to care, forestalling the established benefit of early intervention.MethodsA digital outreach campaign and interactive online care navigation platform was developed and deployed in New York State on October 22, 2020. The campaign offers live connection to a peer or counselor, a self-assessment mental health quiz, and educational material all designed to promote help-seeking in youth and their allies.ResultsBetween October 22, 2020 and July 31, 2021, the campaign resulted in 581,981 ad impressions, 16,665 (2.9%) clicks, and 13,717 (2.4%) unique website visitors. A third (4,562, 33.2%) completed the quiz and 793 (0.1%) left contact information. Of those, 173 (21.8%) completed a virtual assessment and 155 (19.5%) resulted in a referral to care. The median age of those referred was 21 years (IQR = 11) and 40% were considered to be from low-income areas. Among quiz completers, youth endorsing symptoms of depression or anxiety were more likely to leave contact information (OR = 2.18, 95% CI [1.39, 3.41] and OR = 1.69, 95% CI [1.31, 2.19], respectively) compared to those not reporting symptoms of depression or anxiety. Youth endorsing symptoms of psychosis were less likely to report a desire to receive a referral to care (OR = 0.58, 95% CI [0.43, 0.80]) compared to those who did not endorse symptoms of psychosis.ConclusionSelf-reported symptomatology impact trajectories to care, even at the earliest stages of help-seeking, while youth and their allies are searching for information online. An online care navigation team could serve as an important resource for individuals with emerging behavioral health concerns and help to guide the transition between online information seeking at baseline to care.

Electromagnetic Navigation Transthoracic Nodule Localization for Minimally Invasive Thoracic Surgery.

The increased use of chest computed tomography (CT) has led to an increased detection of pulmonary nodules requiring diagnostic evaluation and/or excision. Many of these nodules are identified and excised via minimally invasive thoracic surgery; however, subcentimeter and subsolid nodules are frequently difficult to identify intra-operatively. This can be mitigated by the use of electromagnetic transthoracic needle localization. This protocol delineates the step-by-step process of electromagnetic localization from the pre-operative period to the postoperative period and is an adaptation of the electromagnetically guided percutaneous biopsy previously described by Arias et al. Pre-operative steps include obtaining a same day CT followed by the generation of a three-dimensional virtual map of the lung. From this map, the target lesion(s) and an entry site are chosen. In the operating room, the virtual reconstruction of the lung is then calibrated with the patient and the electromagnetic navigation platform. The patient is then sedated, intubated, and placed in the lateral decubitus position. Using a sterile technique and visualization from multiple views, the needle is inserted into the chest wall at the prechosen skin entry site and driven down to the target lesion. Dye is then injected into the lesion and, then, continuously during needle withdrawal, creating a tract for visualization intra-operatively. This method has many potential benefits when compared to the CT-guided localization, including a decreased radiation exposure and decreased time between the dye injection and the surgery. Dye diffusion from the pathway occurs over time, thereby limiting intra-operative nodule identification. By decreasing the time to surgery, there is a decrease in wait time for the patient, and less time for dye diffusion to occur, resulting in an improvement in nodule localization. When compared to electromagnetic bronchoscopy, airway architecture is no longer a limitation as the target nodule is accessed via a transparenchymal approach. Details of this procedure are described in a step-by-step fashion.

Vision-Based Power Line Segmentation With an Attention Fusion Network

Automatic power transmission line detection plays the key role in smart grid which has been widely applied into the path planning and navigation of intelligent inspection platforms, such as Unmanned Aerial Vehicles (UAVs), climbing robots, hybrid inspection robots, etc. Nevertheless, the power lines are always against complex background environment and different illumination conditions. And the power lines occupy a minimal portion image pixels in the aerial images compared with backgrounds which causes the foreground-background class imbalance issue. Therefore, robust and accurate vision-based power line detection still faces a certain challenge. Recently, deep learning has got fast development on pixel-level object segmentation due to strong contextual feature expression ability, especially U-shape network (U-Net) and its variants. However, it still exists a certain shortcomings owing to insufficient process of local contextual features to affect the segmentation precision. Meanwhile, multiple pooling operations in deep convolutional neural networks (DCNNs) also will cause the information loss. To address these issues, with the encoder-decoder architecture, a novel vision-based power line detection network is proposed in this paper to construct an end-to-end detection scheme of power lines from aerial images. To make the segmentation network capture the global contexts and emphasize target regions of power transmission lines, an attention block is proposed to be embedded into the proposed power line detection network to address the class imbalance issue. Meanwhile, faced with the insufficient process of local contextual feature maps of DCNNs, an attention fusion block is proposed for multi-scale feature fusion to acquire more rich information and improve the segmentation precision. Experiments on power lines show that the proposed power line detection network shows a good segmentation performance on real power line environment compared with other advanced detection methods.

Early Clinical Feasibility Evaluation of an Augmented Reality Platform for Guidance and Navigation during Percutaneous Tumor Ablation

An augmented reality platform with a head-mounted display and electromagnetic tracking of instruments was developed for percutaneous procedural guidance. Earlier work had demonstrated bench and first-in-human feasibility of the platform. This report further evaluated the clinical usability and benefits of this technology. The platform was used in 12 patients who had been referred for percutaneous thermal ablation of abdominal soft tissue tumors. In 10 cases, the intraprocedural holographic guidance agreed with the standard imaging guidance. The evaluation was limited in 2 cases because of anatomic and workflow issues. Overall, this series demonstrated the clinical feasibility of this platform and the potential benefits of its use in percutaneous procedures.

Sacroiliac Joint Fusion: Fusion Rates and Clinical Improvement Using Minimally Invasive Approach and Intraoperative Navigation and Robotic Guidance.

This study adopted a retrospective study design. This study was designed to describe the fusion rate and technique and patient subjective improvement after sacroiliac (SI) joint fusion using a minimally invasive surgical (MIS) approach. The SI joint can mimic radicular or discogenic pain localized to the lower back, gluteal region, or sacral region, posing a challenge in the diagnosis and treatment. This study determines the radiological fusion rate and patient reported subjective clinical outcomes of SI joint fusion using an MIS approach, comparing the use of the Rialto SI joint fusion system (Medtronic, Minneapolis, MN, USA) with the help of the Stealth Navigation System with the use of ExcelsiusGPS Robotic Navigation Platform (Globus Medical Inc., Audubon, PA, USA) using SI-LOK screws (Globus Medical Inc.). In this retrospective study, 43 consecutive patients who underwent SI joint fusion between August 2017 and February 2020 were enrolled; 60 SI joints were fused. The patients' fusion was documented on computed tomography or X-rays, and Visual Analog Scale (VAS) scores were used to determine patient subjective clinical outcomes. A total of 60 joints were fused, including 26 joints fused using robotic guidance and 34 joints fused using the Stealth Navigation System. Student t-test was used to compare the mean preoperative VAS score (7.52±1.3) with the mean postoperative VAS score at the 12-month follow-up (1.43±1.22) (p<0.05). The SI joint fusion rate using this technique was 61% at 6 months, 96.4% at 12 months, and 100% at 18 months. The use of navigation guidance or robotic assistance enables accurate percutaneous screw placement across the SI joint. The use of bone morphogenetic protein in the screw bore hastens fusion across the joint, improving patient-perceived pain.

The Effect of Digital Health Interventions on Parents' Mental Health Literacy and Help Seeking for Their Child's Mental Health Problem: Systematic Review.

BackgroundMany children with mental health problems do not receive professional help. Despite the frequent use of digital health interventions (DHIs) such as websites or web-based service navigation platforms, their effects on parents’ mental health literacy, help seeking, or uptake of professional services are unclear.ObjectiveThis study aims to provide a systematic review and narrative synthesis to describe whether DHIs improve the aforementioned parental outcomes.MethodsDatabases, including CINAHL, Embase, MEDLINE OVID, PsycINFO, and PubMed (2000-2020), were accessed. Studies were included if they evaluated quantitative changes in mental health literacy, help seeking, or the uptake of services by parents of children with mental health problems. Theoretical frameworks, sample sizes, participant demographics, recruitment, interventions, DHI use, results, and health economic measures were used for data extraction.ResultsOf the 11,379 search results, 5 (0.04%) studies met the inclusion criteria. One randomized controlled trial found the reduced uptake of services after using a DHI coupled with a telephone coach for a child’s behavioral problem. Of 3 studies, 2 (66.7%) found statistically significant improvement in mental health literacy for attention-deficit/hyperactivity disorder but had no control group. One study found nonsignificant improvement in mental health literacy and help-seeking attitudes toward anxiety and depression compared with those in active controls. All studies were rated as having a high or serious risk of bias. Search results were affected because of a single reviewer screening articles, overall low-quality studies, and a lack of consistent nomenclature.ConclusionsThere is no high-quality evidence that DHIs can improve parents’ mental health literacy, help seeking, or uptake of services. More research is needed to evaluate DHIs by using rigorous study designs and consistent measures.Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020130074; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020130074