Visual Guidance Research Articles

Development of a Novel Airway-Exchange Broncholaryngoscope.

The purpose of this project was to develop a novel airway-exchange broncholaryngoscope (AEBLScope) to improve the efficiency and accuracy of airway-exchange procedures. The AEBLScope was designed to combine a bronchoscope and airway-exchange catheter (AEC) into a single device and to reduce the blind placement of AECs. The prototype was constructed by modifying an existing distal-chip bronchoscope. A custom AEC was procured to fit concentrically over the flexible portion of the scope. The catheter was connected to the scope handpiece by a customized push-pull locking attachment. The AEBLScope was used to perform airway-exchange procedures with both tracheostomy and endotracheal tubes using two different airway models. Experimental procedures were recorded with still photography to evaluate the exchange of tubes and placement of AECs. In two airway models using the AEBLScope, both tracheostomy and endotracheal tubes were successfully exchanged on first-pass attempt, and AECs were accurately placed under visual guidance. The AEBLScope combines a bronchoscope and AEC into a single tool. Based on these first results, this novel scope has the potential to perform airway-exchange procedures more safely compared with standard procedures by increasing the accuracy of placement, decreasing procedural time, and reducing the morbidity and mortality that can occur from blind placement of AECs. NA Laryngoscope, 134:4488-4493, 2024.

In vivo micro-Raman spectroscopy from an arbitrary-shaped region of interest under simultaneous reflectance confocal imaging guidance

Confocal Raman spectroscopy (CRS) has been widely used for noninvasive biochemical analysis of biological tissue in vivo. Currently, reflectance confocal microscopy (RCM) has been integrated with CRS as visual guidance for a square-/rectangular-shaped region of interest or a point of interest micro-Raman measurement. However, biological structures often have various morphologies; it is highly desirable to acquire a representative Raman spectrum from and only from the area that covers the whole target biological micro-structure. To achieve this goal, we developed a method to acquire Raman spectrum from a region of interest with any (arbitrary) shape under simultaneous RCM imaging guidance.

A Vision-Guided Robotic System for Safe Dental Implant Surgery

Background: Recent advancements in dental implantology have significantly improved outcomes, with success rates of 90–95% over a 10-year period. Key improvements include enhanced preplanning processes, such as precise implant positioning, model selection, and optimal insertion depth. However, challenges remain, particularly in achieving correct spatial positioning and alignment of implants for optimal occlusion. These challenges are pronounced in patients with reduced bone substance or complex anthropometric features, where even minor misalignments can result in complications or defects. Methods: This paper introduces a vision-guided robotic system designed to improve spatial positioning accuracy during dental implant surgery. The system incorporates advanced force-feedback control to regulate the pressure applied to bone, minimizing the risk of bone damage. A preoperative CBCT scan, combined with real-time images from a robot-mounted camera, guides implant positioning. A personalized marker holder guide, developed from the initial CBCT scan, is used for patient–robot calibration. The robot-mounted camera provides continuous visual feedback of the oral cavity during surgery, enabling precise registration of the patient with the robotic system. Results: Initial experiments were conducted on a 3D-printed mandible using a personalized marker holder. Following successful patient–robot registration, the robotic system autonomously performed implant drilling. To evaluate the accuracy of the robotic-assisted procedure, further tests were conducted on 40 identical molds, followed by measurements of implant positioning. The results demonstrated improved positioning accuracy compared to the manual procedure. Conclusions: The vision-guided robotic system significantly enhances the spatial accuracy of dental implants compared to traditional manual methods. By integrating advanced force-feedback control and real-time visual guidance, the system addresses key challenges in implant positioning, particularly for patients with complex anatomical structures. These findings suggest that robotic-assisted implant surgery could offer a safer and more precise alternative to manual procedures, reducing the risk of implant misalignment and associated complications.

Image-Guided Breast Conserving Surgery

Background: Breast-conserving surgery (BCS) has gained wide acceptance as the treatment of choice for early-stage breast cancer. One of the primary goals of BCS is to obtain tumor-free resection margins. In practice, the excision of a palpable breast carcinoma is guided by preoperative diagnostic images and the intraoperative tactile skills of the surgeon. The somewhat ‘blind’ approach of using palpation-guided surgery is known to be highly inaccurate, with studies worldwide reporting positive resection margins in up to 41% of patients. In recent years, ultrasonography has emerged as an effective guidance tool during surgery and ultrasound-guided surgery has been introduced into breast cancer surgery as a method of excising nonpalpable breast cancer. Objective: The aim of this study is comparing ultrasound-guided vs palpation-guided conservative breast surgery in obtaining tumor-free resection margins. Methods: This study was a prospective randomized controlled clinical trial where patients with early breast cancer (T1-T2) and eligible for breast conserving surgery are randomly assigned (1:1) into two arms. Arm A: included the patients who will undergo ultrasound-guided breast conserving surgery. Arm B: included the patients who will undergo the conventional palpation-guided breast conserving surgery. Results: Overall, 142 patients undergoing breast conserving surgery were enrolled: 71 were allocated to the Ultrasound-guided (US) group (Arm A) and 71 to the Palpation-guided (PG) group (Arm B). Patient and tumor characteristics were comparable between the two groups. Age, Body mass index, tumor laterality, tumor location and tumor stage were comparable in both groups and statistically insignificant. Despite that the final tumor size was comparable between both groups, IOUS significantly reduced the main specimen volume and the least negative margin width (p<0.001). The median operative time was significantly longer in the US group bey around 27 min than in the PG group and this was statistically significant in favor of the PG surgery (p<0.001). The involved resection margin rate was significantly higher in the PG group and thus the need for reoperation (p<0.001). Conclusion: IOUS is the only method allowing a true real-time visualization and continuous control of resection margins during all phases of BCS. In our single-institution study, IOUS demonstrated clear superiority over palpation-guided surgery in both oncological and surgical outcomes except for the operative time (smaller excision volume yet, with optimum resection margins). IOUs provides much better tumor localization in small breasts with dense fibroadenosis. Since all the other available localization techniques (including palpation) limit the surgeon’s visual guidance during BCS, IOUS could be regarded as one of the most significant modern technological innovations in the field of breast cancer surgery, restoring sight to the breast surgeon. Our findings strongly suggests that the integration of IOUS in breast conserving surgery could be regarded as a highly beneficial surgical approach.

CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.

Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.

Hub Traveler Guidance Signage Evaluation via Panoramic Visualization Using Entropy Weight Method and TOPSIS

Signage functions as guidance and distribution assistance, directly affecting the operational efficiency of traffic in and around the comprehensive transportation hubs. Among the elements of signage, the visual guidance effect is the key factor affecting the information conveyance, which should be evaluated during the design and optimization process. This study conducted field investigations and developed panoramic videos for multiple transportation hubs in China and designed a survey accordingly. Human subjects were recruited to watch panoramic videos via virtual reality (VR) and respond to the surveys. The results show that the degree of visual attention of travelers significantly affects the evaluation results of guidance signage, with the influence being inversely proportional. Key factors affecting visual attention include accurate legibility, obstruction and defacement rates, informativeness, and whether signage is set up in a hierarchical manner. In unfamiliar environments, travelers focus on the overall context and closely observe the interaction between directional signs and their surroundings. The prominence and visibility of signage are influenced by interactions within the spatial environment. Notably, simple and clear signs are more likely to attract travelers’ attention, and their directional information is more easily comprehended. Moreover, when the destination is clearly defined, visual attention significantly directs pedestrians’ wayfinding behavior.

IMPROVING ROAD SAFETY AT ACCIDENT-PRONE AREAS: A COMPARISON BETWEEN GLOW-IN-THE-DARK AND CONVENTIONAL ROAD MARKING

Driving at night is challenging due to poor vision, poor road visual guidance, and the need to encounter bright light sources. Factors like lack of street lighting, fading, and lack of reflectivity by road studs and retro-reflective materials affect road users' vision at night. Commentators have pointed out there is a critical demand to improve road safety in preventing road accidents and hazards that caused by the poor visibility of road markings and inadequacy lighting. Although road safety management has been given an important focus by the Ministry of Transportation Malaysia to achieve 50% reduction of road accidents by 2030, there is a critical demand to improve the present road markings method to overcome the issues of poor visibility or unclear road markings and inadequacy lighting that may trigger potential hazards to road users at night, with a new innovative road marking technology. Therefore, this paper is prepared with the objective to present the outcome of comparative study between the present conventional road markings and the newly innovative technology of glow-in-the-dark method with specific reference to the road safety management in Malaysia. This study employed a questionnaire survey to interview fifty-one (51) respondents that have experience of driving at night, with the objectives to get their opinions on present condition of road markings in Malaysia, and how the newly innovative technology glow-in-the-dark can improve road safety in Malaysia. The study outcome revealed that the glow-in-the-dark is deemed appropriate to enhance the visibility during low-light conditions as compared to the present conventional road markings. Although the initial cost is higher, the economic advantages of glow-in-the-dark in reducing the streetlight usage, lower electrical costs, and substantially extended lifespan have made this newly innovative road marking outweighed the downsides and economically wise choice to revolutionize road safety management practice at the accident-prone areas in Malaysia for achieving the SDG 3: Good Health and Well-being.

CRISP: A cross-modal integration framework based on the surprisingly popular algorithm for multimodal named entity recognition

The multimodal named entity recognition task on social media involves recognizing named entities with textual and visual information, which is of great significance for information processing. Nevertheless, many existing models still face the following challenges. First, in the process of cross-modal interaction, the attention mechanism sometimes focuses on trivial parts in the images that are not relevant to entities, which not only neglects valuable information but also inevitably introduces visual noise. Second, the gate mechanism is widely used for filtering out visual information to reduce the influence of noise on text understanding. However, the gate mechanism neglects capturing fine-grained semantic relevance between modalities, which easily affects the filtration process. To address these issues, we propose a cross-modal integration framework based on the surprisingly popular algorithm, aiming at enhancing the integration of effective visual guidance and reducing the interference of irrelevant visual noise. Specifically, we design a dual-branch interaction module that includes the attention mechanism and the surprisingly popular algorithm, allowing the model to focus on valuable but overlooked parts in the images. Furthermore, we compute the matching degree between modalities at the multi-granularity level, using the Choquet integral to establish a more reasonable basis for filtering out visual noise. We have conducted extensive experiments on public datasets, and the experimental results demonstrate the advantages of our model.

Firefighting Robot Extinguishment Decision‐Making Based on Visual Guidance: A Novel Attention and Scale U‐Net Model and Genetic Algorithm

ABSTRACTAt present, rescue firefighting relies mainly on firefighting robots, and robots with perception and decision‐making functions are the key elements for achieving intelligent firefighting. However, traditional firefighting robots often lack the ability for autonomous perception and decision‐making when extinguishing multiple fire sources, leading to low rescue efficiency and increased risk for rescue personnel, especially when making firefighting decisions in extreme fire scenes, which poses a challenge. To effectively handle firefighting tasks and ensure operational efficiency, a robot firefighting decision‐making method based on drone visual guidance is proposed. First, we introduce a novel Attention and Scale U‐Net (ASUNet) model to accurately capture crucial target information, including fire location and size, in a fire scene. The ASUNet model adopts an effective multiscale fusion strategy and attention mechanism to enhance the model's performance. Subsequently, based on the results of the ASUNet model, through pixel segmentation clustering and a genetic optimization algorithm, we obtain the robot's firefighting decision results, thereby guiding the robot to carry out firefighting operations systematically. Finally, through numerical experiments, it is verified that the proposed ASUNet model is superior and effective, as the model can perceive important information in a fire scene and extract it well. The use of improved genetic optimization can further accelerate algorithm convergence. To our knowledge, this study is the first to use drone‐based monocular vision guidance for firefighting decision‐making, providing significant engineering value.

MAFNet: a two-stage multiple attention fusion network for partial-to-partial point cloud registration

Abstract 3D point cloud registration is a critical technology in the fields of visual measurement and robot automation processing. In actual large-scale industrial production, the accuracy of point cloud registration directly affects the quality of automated welding processes. However, most existing methods are confronted with serious challenges such as the failure of partial-to-partial point cloud model registration when facing robot automatic processing guidance and error analysis work. Therefore, this paper proposes a novel two-stage network architecture for point cloud registration, which aims at robot pose adjustment and visual guidance in the field of automated welding by using 3D point cloud data. Specifically, we propose a neighborhood-based multi-head attention module in the coarse registration stage. The neighborhood information of each point can be aggregated through focusing on different weight coefficients of multi-head inputs. Then the spatial structure features which is used to establish the overlapping constraint of point clouds are obtained based on above neighborhood information. In the fine registration stage, we propose the similarity matching removal module based on multiple attention fusion features to explore deeper features from different aspects. By using deep fusion features to guide the similarity calculation, the interference of non-overlapping points is removed to achieve the finer registration. Eventually, we compare and analyze the proposed method with the SOTA ones through several error metrics and overlap estimation experiments based on the ModelNet40 dataset. The test results indicate that our method, relative to other mainstream techniques, achieves lower error rates and the most superior accuracy of 98.61% and recall of 98.37%. To demonstrate the generalization performance of proposed algorithm, extensive experiments on the Stanford 3D Scanning Repository, 7-Scenes and our own scanning dataset using partially overlapping point clouds individually under clean and noisy conditions show the validity and reliability of our proposed registration network.

Research on a Novel Citrus Reticulata ‘Chachi’ Orientation Adjustment Mechanism (COAM) and Machine Vision Guidance Control

The initial processing of Citrus Reticulata ‘Chachi’ involves peeling as a crucial step. Currently, there is some semi-automatic peeling equipment available. However, due to the requirement of adjusting the orientation of Citrus Reticulata ‘Chachi’ to ensure the stem (or navel) is facing upwards before peeling and because the peeling process must retain the stem as a marker for fresh fruit picking, the loading of Citrus Reticulata ‘Chachi’ for peeling still solely relies on manual operation, resulting in low efficiency and poor standardization. With the rapid growth of the pericarp of the Citrus Reticulata ‘Chachi’ industry, semi-automatic processing equipment is no longer able to meet production demands. The loading issue before peeling Citrus Reticulata ‘Chachi’ is a complex hand–eye coordination problem. In response to this issue, this paper proposes a novel Citrus Reticulata ‘Chachi’ orientation adjustment mechanism (COAM). This mechanism utilizes frictional force to adjust the orientation of Citrus Reticulata ‘Chachi’. First, the conceptual design and kinematic modelling analysis of the mechanism were conducted. Next, the omnidirectional friction-driven wheels were optimized in design. Subsequently, a prototype was manufactured and assembled to conduct validation tests on its open-loop motion performance. Finally, a visual feedback-guided algorithm was introduced to complement the kinematic model, enabling the automatic and rapid adjustment of Citrus Reticulata ‘Chachi’ orientation. The experimental results indicate that the COAM designed in this study can effectively and rapidly adjust the orientation of Citrus Reticulata ‘Chachi’ fruits of different sizes and shapes. It demonstrates strong adaptability, and under visual feedback guidance, the orientation adjustment error is less than 10% of the fruit’s diameter. This meets the requirements for automated production in the initial processing of Citrus Reticulata ‘Chachi’. The research presented in this paper also provides new insights for the orientation adjustment and loading of similar spherical fruits.

Visual guidance technology of flying cars based on multilevel markers and depth

Split-type flying car will play an important role in the future transportation. This paper adopts a guidance method that couples visual information and depth information, and improves the docking accuracy through the mutual cooperation of the drone and the vehicle. Firstly, a multilevel docking marker is designed to achieve adaptive target matching within different distances during the docking process. The marker has strong robustness and can adapt to complex scenes such as occlusion, strong light, and large angle tilting, providing the redundant corner points required for machine vision detection pose information accurately. Secondly, a three-dimensional pose estimation algorithm is proposed, which can introduce depth information to correct the homography matrix. The algorithm combines the advantages of strong robustness to multilevel marker detection and high accuracy of depth information, and can output millimeter-level precision pose information in different environments, different inclination angles, and different occlusions. Finally, a flying car model experiment was carried out, and the results showed that the guidance technology can obtain millimeter-level precise pose information during the entire process of long distance-near distance-completion of docking, thus realizing precise docking.

Enhancing motor imagery in the third-person perspective by manipulating sense of body ownership with virtual reality.

Virtual reality (VR)-guided motor imagery (MI) is a widely used approach for motor rehabilitation, especially for patients with severe motor impairments. Most approaches provide visual guidance from the first-person perspective (1PP). MI training with visual guidance from the third-person perspective (3PP) remains largely unexplored. We argue that 3PP MI training has its own advantages and can supplement 1PP MI. For some movements beyond the view of 1PP, such as shoulder shrugging and other axial movements, MI are suitable performed under 3PP. However, the efficiency of existing paradigms for 3PP MI is unsatisfactory. We speculate that the absence of sense of body ownership (SOO) from 3PP could be one possible factor and hypothesize that 3PP MI could be enhanced by eliciting SOO over a 3PP avatar. Based on our hypothesis, a novel paradigm was proposed to enhance 3PP MI by inducing full-body illusion (FBI) from 3PP, which is similar to the so-called out-of-body experience (OBE), using synchronous visuo-tactile stimulus with VR. The event-related Electroencephalograph (EEG) desynchronization (ERD) at motor-related regions from 31 healthy participants were calculated and compared with a control paradigm without "OBE" FBI induction. This study attempts to enhance 3PP MI with FBI induction. It offers an opportunity to perform MI guided by action observation from 3PP with elicited SOO to the observed avatar. We believe that 3PP MI could provide more possibilities for effective rehabilitation training, when SOO could be elicited to a virtual avatar and the present work demonstrates its viability and effectiveness.

Wirelessly Powered Visible Light-Emitting Implant for Surgical Guidance during Lumpectomy.

Achieving negative surgical margins, defined as no tumor found on the edges of the resected tissue, during lumpectomy for breast cancer is critical for mitigating the risk of local recurrence. To identify nonpalpable tumors that cannot be felt, pre-operative placements of wire and wire-free localization devices are typically employed. Wire-free localization approaches have significant practical advantages over wired techniques. In this study, we introduce an innovative localization system comprising a light-emitting diode (LED)-based implantable device and handheld system. The device, which is needle injectable and wire free, utilizes multiple wirelessly powered LEDs to provide direct visual guidance for lumpectomy. Two distinct colors, red and blue, provide a clear indication of tissue depth: blue light is absorbed strongly in tissue, visible within a close range of <1 cm, while red light remains visible through several centimeters of tissue. The LEDs, integrated with an impedance-matching circuit and receiver coil, are encapsulated in biocompatible epoxy for injection with a 12 G needle. Our findings demonstrate that the implant exhibits clearly perceivable depth-dependent color changes and remains visible through >2 cm of ex vivo chicken breast and bovine muscle tissue using less than 4 W of transmitted power from a handheld antenna. These miniaturized needle-injectable localization devices show promise for improving surgical guidance of nonpalpable breast tumors.

Intraoperative laparoscopic ultrasound during laparoscopic myomectomy: a narrative review.

Intraoperative laparoscopic ultrasound (IOLUS), a dynamic imaging technique, has emerged as a valuable instrument for guiding surgery in various medical specialties. As IOLUS provides accuracy, improved visualization, and real-time guidance, the integration of IOLUS into many surgical procedures has occurred and IOLUS assists surgeons during advanced procedures. Today, laparoscopic myomectomy has become a prominent surgical procedure in gynecology. Despite its benefits, laparoscopic myomectomy presents certain challenges. The risk of residual fibroids is higher in laparoscopic myomectomy compared to abdominal surgery. The limited depth perception and restricted range of motion can also be obstacles for surgeons, especially when dealing with deeply embedded fibroids. IOLUS has the potential to overcome these limitations. In this study, our aim was to conduct a review of the literature concerning the use of IOLUS during laparoscopic myomectomy.

A Novel Fusion Perception Algorithm of Tree Branch/Trunk and Apple for Harvesting Robot Based on Improved YOLOv8s

Aiming to accurately identify apple targets and achieve segmentation and the extraction of branch and trunk areas of apple trees, providing visual guidance for a picking robot to actively adjust its posture to avoid branch trunks for obstacle avoidance fruit picking, the spindle-shaped fruit trees, which are widely planted in standard modern apple orchards, were focused on, and an algorithm for apple tree fruit detection and branch segmentation for picking robots was proposed based on an improved YOLOv8s model design. Firstly, image data of spindle-shaped fruit trees in modern apple orchards were collected, and annotations of object detection and pixel-level segmentation were conducted on the data. Training set data were then augmented to improve the generalization performance of the apple detection and branch segmentation algorithm. Secondly, the original YOLOv8s network architecture’s design was improved by embedding the SE module visual attention mechanism after the C2f module of the YOLOv8s Backbone network architecture. Finally, the dynamic snake convolution module was embedded into the Neck structure of the YOLOv8s network architecture to better extract feature information of different apple targets and tree branches. The experimental results showed that the proposed improved algorithm can effectively recognize apple targets in images and segment tree branches and trunks. For apple recognition, the precision was 99.6%, the recall was 96.8%, and the mAP value was 98.3%. The mAP value for branch and trunk segmentation was 81.6%. The proposed improved YOLOv8s algorithm design was compared with the original YOLOv8s, YOLOv8n, and YOLOv5s algorithms for the recognition of apple targets and segmentation of tree branches and trunks on test set images. The experimental results showed that compared with the other three algorithms, the proposed algorithm increased the mAP for apple recognition by 1.5%, 2.3%, and 6%, respectively. The mAP for tree branch and trunk segmentation was increased by 3.7%, 15.4%, and 24.4%, respectively. The proposed detection and segmentation algorithm for apple tree fruits, branches, and trunks is of great significance for ensuring the success rate of robot harvesting, which can provide technical support for the development of an intelligent apple harvesting robot.

Hierarchical visual semantic guidance for enhanced relationship recognition in domain knowledge graphs

Multi-modal entity relationship recognition is a crucial foundation for constructing accurate and comprehensive domain knowledge graphs. However, due to the sparse and diverse nature of textual semantic information and the semantic differences exhibited by multi-modal data, existing methods often suffer from issues such as information loss and noise features. In this paper, the Hierarchical Visual Semantic Guidance (HVSG) Network is proposed, which utilizes images to furnish visual semantic information for text, facilitating the identification of implicit relationships between entities. Specifically, salient local instance objects are initially extracted from the global image, followed by the use of a hierarchical visual semantic construction module to establish multi-level visual semantics. The integration of multi-level visual and textual features is then achieved through a visual semantic guidance module. This work constructed a Chinese multi-modal entity relation classification dataset in the domain of unmanned vessels and conducted experiments on two datasets. Compared to state-of-the-art (SOTA) models, HVSG achieved the best performance, with F1 scores improved by 1.05% and 0.58% respectively, indicating that HVSG can offer better performance in exploring implicit relationships between textual entities.

Measuring User Response to Attention Guidance Using the Integrated Cognitive User Assistance System

Humans are limited in their attentional capacity and miss salient cues when multitasking. We built a platform, ICUa (Integrated Cognitive User assistance) that simulates parts of MATBII, a computer-based multitasking testbed, adding gaze-based attention guidance in an attempt to improve multitasking performance. ICUa uses software agents for monitoring the interface, human behavior and eye gaze in order to guide attention (displaying highlights and arrows). We varied the difficulty of our testbed and found effects on human performance (based on error times) and characterized the types of errors that occurred with higher difficulty and how agent response changed. There was no clear effect of ICUa guidance on gaze patterns or human performance. The visual guidance we deployed may not be sufficient to measurably influence behavior. Future work will develop the detection eye gaze patterns and effective cues to redirect attention using this human-agent interface for experiments on attention guidance.

Design of a Leaf-Bottom Pest Control Robot with Adaptive Chassis and Adjustable Selective Nozzle

Pest control is an important guarantee for agricultural production. Pests are mostly light-avoiding and often gather on the bottom of crop leaves. However, spraying agricultural machinery mostly adopts top-down spraying, which suffers from low pesticide utilization and poor insect removal effect. Therefore, the upward spraying mode and intelligent nozzle have gradually become the research hotspot of precision agriculture. This paper designs a leaf-bottom pest control robot with adaptive chassis and adjustable selective nozzle. Firstly, the adaptive chassis is designed based on the MacPherson suspension, which uses shock absorption to drive the track to swing within a 30° angle. Secondly, a new type of cone angle adjustable selective nozzle was developed, which achieves adaptive selective precision spraying under visual guidance. Then, based on a convolutional block attention module (CBAM), the multi-CBAM-YOLOv5s network model was improved to achieve a 70% recognition rate of leaf-bottom spotted bad point in video streams. Finally, functional tests of the adaptive chassis and the adjustable selective spraying system were conducted. The data indicate that the adaptive chassis can adapt to diverse single-ridge requirements of soybeans and corn while protecting the ridge slopes. The selective spraying system achieves 70% precision in pesticide application, greatly reducing the use of pesticides. The scheme explores a ridge-friendly leaf-bottom pest control plan, providing a technical reference for improving spraying effect, reducing pesticide usage, and mitigating environmental pollution.

Conventional ultrasonography enabled with augmented reality needle guidance for percutaneous kidney access: An innovative methodologies randomized controlled trial.

Successful needle puncture of the renal collecting system is a critical but difficult procedure in percutaneous nephrolithotomy (PCNL). Although fluoroscopy and ultrasound are the standard imaging techniques to guide puncture during PCNL, both have known limitations. To assess the feasibility and safety of a new navigation system for needle puncture in ultrasound-guided PCNL. This study employed a single-center randomized controlled trial (RCT) design to assess the feasibility and safety of a new navigation system for needle puncture in ultrasound-guided PCNL. Conducted between May 2021 and November 2021, the trial utilized computer-generated random numbers for participant allocation to control for selection bias. The trial was executed at the *********, which serves as an academic medical center. All patients who met the inclusion criteria were randomly divided into two groups, with 29 patients in each group. One group underwent PCNL procedures using the new navigation system, while the control group underwent standard ultrasound-guided PCNL procedures. Included patients had renal pelvis or caliceal calculi larger than 2.0cm in diameter or had multiple or staghorn stones. The puncture procedure was performed with the support of real-time ultrasound imaging and visual guidance displayed on the screen. The primary outcome was system feasibility and puncture success rate. Secondary outcomes included puncture time, total surgical time, number of attempts, post-procedure complications, and one-year and three-year stone recurrence rates. Stone clearance was defined by postoperative CT. Descriptive statistics summarized patient demographics, stone size, and location. Independent samples t-tests analyzed puncture time and total surgical time. Chi-square or Fisher's exact tests compared stone clearance, complications, socioeconomic status, renal hydronephrosis, stone location, race, and medical history. Linear regression examined the correlation between BMI and puncture time. Significance was set at P<0.05. For all 58 patients undergoing PCNL, needle punctures of the renal collecting system were completed with a success rate of 100%. The average time from planning the puncture protocol to successful puncture was significantly shorter in the AcuSee guidance system group (3.12min, range 0.2-6.88min) compared to the standard ultrasound-guided group (7.58min, range 5.41-10.68min), representing a reduction of approximately 59%. The total surgical time was also shorter in the AcuSee group for patients with no and mild hydronephrosis (P<0.05). Complication rates were lower in the AcuSee group, with no major complications observed. However, 3 patients in the standard ultrasound-guided group have adverse effects after the PCNL procedure. The one-year stone recurrence rate was significantly lower in the AcuSee group (3.4%) compared to the standard group (24.1%), and the three-year recurrence rate was also lower (6.9% vs. 41.4%). Patient-specific factors such as BMI, renal morphology, and prior surgical history did not significantly affect the performance of the AcuSee system. We report the first clinical application of a new navigation system for needle puncture in ultrasound-guided PCNL. It has been demonstrated that it is feasible and safe compared to the standard ultrasound-guided group in percutaneous renal puncture. This technology provides intuitive and easy-to-use visual guidance, which may facilitate safe, accurate and fast needle puncture of the kidney.