Search Results (530)

The paper introduces a computationally efficient semantic-aware route planning framework for indoor mobile robots, designed for real-time execution on resource-constrained edge hardware (Raspberry Pi 5, CPU-only). The proposed architecture fuses monocular object detection with 2D LiDAR-based range estimation and integrates the resulting semantic annotations into the Nav2 Route Server for penalty-weighted route selection. Object localization in the map frame is achieved through the Angular Sector Fusion (ASF) pipeline, a deterministic geometric method requiring no parameter tuning. The ASF projects YOLO bounding boxes onto LiDAR angular sectors and estimates the object range using a 25th-percentile distance statistic, providing robustness to sparse returns and partial occlusions. All intrinsic and extrinsic sensor parameters are resolved at runtime via ROS 2 topic introspection and the URDF transform tree, enabling platform-agnostic deployment. Detected entities are classified according to mobility semantics (dynamic, static, and minor) and persistently encoded in a GeoJSON-based semantic map, with these annotations subsequently propagated to navigation graph edges as additive penalties and velocity constraints. Route computation is performed by the Nav2 Route Server through the minimization of a composite cost functional combining geometric path length with semantic penalties. A reactive replanning module monitors semantic cost updates during execution and triggers route invalidation and re-computation when threshold violations occur. Experimental evaluation over 115 navigation segments (legs) on three heterogeneous robotic platforms (two single-board RPi5 configurations and one dual-board setup with inference offloading) yielded an overall success rate of 97% (baseline: 100%, adaptive: 94%), with 42 replanning events observed in 57% of adaptive trials. Navigation time distributions exhibited statistically significant departures from normality (Shapiro–Wilk, p < 0.005). While central tendency differences between the baseline and adaptive modes were not significant (Mann–Whitney U, p = 0.157), the adaptive planner reduced temporal variance substantially (σ = 11.0 s vs. 31.1 s; Levene’s test W = 3.14, p = 0.082), primarily by mitigating AMCL recovery-induced outliers. On-device YOLO26n inference, executed via the NCNN backend, achieved 5.5 ± 0.7 FPS (167 ± 21 ms latency), and distributed inference reduced the average system CPU load from 85% to 48%. The study further reports deployment-level observations relevant to the Nav2 ecosystem, including GeoJSON metadata persistence constraints, graph discontinuity (“path-gap”) artifacts, and practical Route Server configuration patterns for semantic cost integration. Full article

Background/Objectives: Unilateral condylar hyperplasia is an idiopathic condition that causes facial asymmetry and occlusal problems. Currently, traditional treatment protocol is the combination of orthognathic and extra-oral condylectomy surgery via pre-auricular incision, which can create aesthetic problems with additional risks of facial nerve damage. The purpose of this study was to report management of condylar hyperplasia patients through minimally invasive condylectomy that was planned via virtual methods. Methods: The custom-made cutting guides were produced, and unilateral condylectomy operations were performed via intra-oral approach. Orthognathic surgery with/without genioplasty operations were either done with condylectomy in one session or in an additional session. Results: Custom-made cutting guides produced by virtual methods provided easy operations without any need for additional extra-oral incisions. Planned osteotomies were transferred successfully from the virtual surgical plan and resections of the excess bone tissues were performed using novel piezo surgery devices. The bones were fixed to their pre-planned position using 3D-printed titanium plates. The patients healed without any complications. Results of this innovative virtually guided protocol tested showed functional and esthetic results without any extra-oral scars with no facial nerve damage. Conclusions: Combination of intra-oral condylectomy with orthognathic surgery using 3D-printed titanium cutting guides seems to be an advantageous approach with successful results in terms of aesthetics and function for management of mandibular condylar hyperplasia patients; however, there is an urgent need in the scientific literature for further clinical research with a larger number of subjects. Full article

Background: Artificial intelligence-based computer-aided detection (AI-CAD) systems are increasingly being used in endovascular practice to support time-sensitive detection, triage and prioritization tasks in imaging and procedural workflows. Despite rapid technological advancements and expanding regulatory clearances, the translation to lasting clinical benefit varies. Objective: This narrative review synthesizes AI-CAD applications in endovascular interventions and proposes an evaluation-oriented framework to support responsible clinical translation; this framework emphasizes detection-specific metrics, external validation, bias-aware assessment, and workflow integration. Methods: A structured narrative review was conducted using targeted searches in PubMed, Google Scholar, and IEEE Xplore (2020–2026); this review was supported by an examination of US FDA device databases and citation tracking. Evidence was assessed using a pragmatic hierarchical classification framework based on regulatory status and validation rigor. Results: AI-CAD applications were mapped across four main endovascular domains: neurovascular interventions (e.g., large vessel occlusion triage), coronary interventions (CCTA-based stenosis detection and intravascular imaging support), aortic interventions/EVAR (endoleak detection and sac monitoring), and peripheral interventions (lesion detection and angiographic decision support). Across the domains, performance reporting was heterogeneous and often relied on retrospective, single-center assessments. Key barriers to clinical readiness included acquisition variability and dataset shift due to artifacts, limited multicenter validation, annotation variability, and human–AI workflow factors. Evaluation priorities included whether to assess at the lesion level or case level, false positive burden and calibration, external validation under real-world heterogeneity, and clinical impact measures such as treatment timing and procedural decision-making. Conclusions: AI-CAD systems hold significant potential for improving endovascular care; however, clinical readiness depends on rigorous, endovascular feature-specific assessment and transparent reporting, beyond retrospective accuracy. The proposed evidence level framework and assessment checklist provide practical tools for distinguishing mature technologies from research prototypes and guiding future validation, implementation, and post-market monitoring. Full article

This study investigated the impact of walking with ankle-foot-orthoses (AFOs) and without AFOs (non-AFO) on ground reaction forces (GRFs) in patients with peripheral artery disease (PAD). Additionally, this study examined the effect of AFO intervention vs. no AFO intervention on GRFs while walking with and without AFOs. Fifty patients with PAD were randomly assigned to either a three-month intervention (AFO) or a control (standard-of-care) group. After three months, subjects crossed over to the alternate group and were evaluated after three additional months. GRF data (anterior-posterior, medial-lateral, and vertical) were collected during walking with and without AFOs at baseline, three, and six months. Peak discrete GRF points, braking and propulsion impulses were compared across conditions, groups, and time points using linear mixed models. The peak brake and propulsion GRF were significantly reduced while walking with AFOs versus non-AFO (p < 0.01). Compared to non-AFO, walking with AFOs significantly reduced lateral GRF magnitude (p = 0.03) and significantly increased medial GRF (p = 0.02). The first and second maximum (p < 0.01) vertical GRF were significantly increased with AFOs versus non-AFOs. Walking with AFOs helped patients with PAD achieve greater peak propulsion and vertical GRFs compared to non-AFO, with GRF values trending toward those previously reported in healthy individuals. Full article

We investigated the effects of manipulating visual information on motor control indicators during the Waiter’s Bow Test. The results suggested that visual information occlusion reduced the maximum flexion angles of the lumbar spine and upper lumbar region. Furthermore, subjects who tested negative under the open-eye condition tested positive under the closed-eye condition. Regarding muscle activity in the rectus abdominis and erector spinae muscles, it was suggested that this activity was not affected by visual information. These findings indicate that visual sensory feedback is one factor influencing lumbar motor control. The integration of electromyography and accelerometer systems in this study highlights the role of wearable sensor technologies in quantifying neuromuscular function in Bioengineering. By restricting visual information, a model for sensory reweighting can be established for the design of biofeedback systems, rehabilitation robotics, and assistive devices. The results of this study demonstrate how sensor-based evaluation and sensory manipulation can inform the engineering of diagnostic and therapeutic technologies for motor control assessment. Full article

Background and Objectives: Massive extremity haemorrhage remains the leading cause of preventable death on the battlefield and among trauma victims in civilian settings. Tourniquets are an effective, low-cost tool used to rapidly control bleeding. However, the availability of certified tourniquet models during a full-scale armed conflict can be significantly limited. This favours the emergence of locally manufactured devices. The aim of this study was to compare the effectiveness of the DNIPRO Gen 2 and SICH tourniquets with the CAT Gen 7 and SOFTT W Gen 4 tourniquets recommended by the Committee on Tactical Combat Casualty Care. Materials and Methods: The study included 51 Ukrainian medics experienced in prehospital care. Application speed was measured with a stopwatch, and occlusion success was confirmed by Doppler ultrasound. Pain was measured using the NRS, and participants were also able to provide subjective comments regarding the ergonomics and design of the tourniquets. Results: The four tourniquets tested demonstrated different occlusion success rates in arm and leg application. In upper extremity application, the SICH had the highest success rate (98.0%) and was associated with higher odds of successful application compared with the SOFTT-W Gen 4 (OR 25.14). In lower extremity application, the CAT Gen 7 had the highest rate of success (94.1%) and was 7.5 times more likely to achieve occlusion than the SOFTT-W Gen 4 (OR 7.54). The DNIPRO Gen 2 was rated most painful (Median 6), with significantly lower pain levels reported for the SICH (Median 4), the CAT Gen 7 (Median 5), and the SOFTT-W Gen 4 (Median 4). Conclusions: The DNIPRO Gen 2 and SICH tourniquets demonstrated high occlusion success rates, comparable to the CAT Gen 7 and superior to the SOFTT-W Gen 4. These findings suggest that Ukrainian-manufactured tourniquets may demonstrate comparable performance to CoTCCC-recommended tourniquets in a simulated prehospital setting. Full article

Background/Objectives: This GLP (Good laboratory practice) study evaluates the MRI compatibility and occlusion performance of the Nuvascular Harbor intrasaccular device for the treatment of bifurcation and sidewall aneurysms in a rabbit aneurysm model. Methods: A total of 27 New Zealand White rabbits with 33 surgically created aneurysms (22 bifurcation, 11 side wall) were included and allocated to 90-day (n = 12) or 180-day (n = 15) follow-up. After exclusion of one aneurysm due to parent vessel occlusion and one aneurysm unsuitable for treatment, 31 treated aneurysms remained for analysis. All animals underwent DSA and 3T MRI, including TOF-MRA, FLAIR, DWI, and SWI sequences. Occlusion status was independently graded using the Raymond–Roy Occlusion Classification (RROC), and intermodality agreement was assessed. Results: MR-based occlusion assessment demonstrated strong agreement with DSA, with exact Raymond–Roy class concordance in 80.6% of cases and clinically relevant agreement (adequate vs. incomplete occlusion) in 96.8%. Agreement analysis showed substantial concordance (Cohen’s κ = 0.65) and a strong positive correlation (r = 0.79). Adequate occlusion rates were comparable between modalities (87.1% on MRA vs. 83.9% on DSA), supporting the reliability of MR imaging for non-invasive occlusion assessment, reflecting consistent device visibility on MR imaging. Conclusions: The Harbor device provides a promising solution for follow up aneurysm occlusion with increased MR visibility, enabling safer, contrast- and radiation-free follow-up. This study emphasizes the need for future endovascular devices to integrate imaging compatibility into their design to enhance long-term patient follow up. Full article

Systemic lupus erythematosus (SLE) frequently presents joint pain and stiffness, yet clinicians lack an objective, rapid method to quantify joint inflammation at the point of care. We introduce the Lupus Optical Tomography Imaging System (LOTIS), a wearable near-infrared (NIR) device that performs real-time three-dimensional tomographic imaging of hemodynamic changes in finger joints. LOTIS was developed to address key limitations of our earlier Flexible Optical Imaging System (FOIS), including mechanical fragility, high noise levels, single-joint acquisition, and slow reconstruction times. The new system integrates modular, mechanically robust optical patches with on-sensor digitization and a computationally efficient, non-iterative multispectral reconstruction algorithm to produce frame-by-frame maps of hemoglobin concentration. In a preliminary study using a standardized venous-occlusion protocol, LOTIS differentiated SLE-affected joints from those of healthy controls. Diseased joints exhibited blunted and spatially diffuse hemodynamic responses, whereas healthy joints showed localized and robust changes. These results demonstrate that LOTIS provides an operator-independent, patient-friendly method for quantifying joint-specific hemodynamic changes in real time, offering strong potential as a clinical tool for objective assessment and longitudinal monitoring of lupus arthritis. Full article

To address the issues of poor adaptability to complex scenarios, high computational complexity, and difficulties in terminal deployment of existing steel surface defect detection models, a novel lightweight detection network named LHO-net is proposed, with the Lightweight Multi-Backbone (LM Backbone), the Hierarchical Scale-based Pyramid Attention Network (HSPAN), and the Occlusion-aware Detection Head (OAHead). The LM Backbone adopts a dual-branch structure with shared HGStem and a dynamic feature fusion mechanism, effectively capturing multi-dimensional features of irregular defects while extremely compressing model parameters. The HSPAN module realizes efficient fusion of multi-scale features through dynamic feature selection and adaptive upsampling strategies, balancing background noise suppression and defect detail preservation. The OAHead completes adaptive compensation of features in occluded regions by means of deep feature aggregation and exponential normalization technology, significantly enhancing the ability to recognize complex defects. On the NEU-DET dataset, LHO-net achieves a mAP@0.5 of 75.0%, a mAP@0.5:0.95 of 44.0%, and a recall of 73.6%, with a computational complexity of only 2.3 GFLOPS. Compared with the baseline model YOLOv12, it reduces parameters by 64% and computational cost by 60.3%. On the GC-10 dataset, its mAP@0.5 reaches 67.2%, and its detection stability for complex defects such as slender creases and low-contrast water spots is superior to that of mainstream lightweight YOLO variants. Visualization results confirm that the model can effectively avoid common problems such as redundant annotations and false detections and maintains stable recognition performance for various defects. It solves the core contradiction between detection accuracy and lightweight deployment in industrial scenarios, providing an efficient and practical technical solution for real-time steel surface defect detection on resource-constrained terminal devices. Full article

Severe foliage occlusion and dynamically changing lighting conditions in complex orchard environments pose significant challenges for visual perception systems in automated apple harvesting, including low detection accuracy, poor robustness, and insufficient real-time performance. To address these issues, this study proposes an improved lightweight detection network based on YOLOv11, named YOLO-WBL, along with a precise yield estimation algorithm based on 3D point clouds, termed CLV. The YOLO-WBL network is optimized in three aspects: (1) A C3K2_WT module integrating wavelet transform is introduced into the backbone network to enhance multi-scale feature extraction capability; (2) A weighted bidirectional feature pyramid network (BiFPN) is adopted in the neck network to improve the efficiency of multi-scale feature fusion; (3) A lightweight shared convolution separated batch normalization detection head (Detect-SCGN) is designed to significantly reduce the parameter count while maintaining accuracy. Based on this detection model, the CLV algorithm deeply integrates depth camera point cloud information through 3D coordinate mapping, irregular point cloud reconstruction, and convex hull volume calculation to achieve accurate estimation of individual fruit volume and total yield. Experimental results demonstrate that: (1) The YOLO-WBL model achieves a precision of 93.8%, recall of 79.3%, and mean average precision (mAP@0.5) of 87.2% on the apple test set; (2) The model size is only 3.72 MB, a reduction of 28.87% compared to the baseline model; (3) When deployed on an NVIDIA Jetson Xavier NX edge device, its inference speed reaches 8.7 FPS, meeting real-time requirements; (4) In scenarios with an occlusion rate below 40%, the mean absolute percentage error (MAPE) of yield estimation can be controlled within 8%. Experimental validation was conducted using apple images selected from the dataset under varying lighting intensities and fruit occlusion conditions. The results demonstrate that the CLV algorithm significantly outperforms traditional average-weight-based estimation methods. This study provides an efficient, accurate, and deployable visual solution for intelligent apple harvesting and yield estimation in complex orchard environments, offering practical reference value for advancing smart orchard production. Full article

Identifying the plant species comprising a pasture, among other aspects, is crucial for assessing its nutritional value for grazing animals and facilitating its effective management. Traditionally, it requires labor-intensive visual inspection. Artificial Intelligence (AI) offers a solution for automatic classification, yet robust datasets for training such models in natural, uncontrolled environments are scarce. This data descriptor presents a dataset of 741 images collected in pasture lands in the Centre of Portugal using standard cameras at a height of 50 cm. A semi-automated annotation pipeline was employed, utilizing a Faster R-CNN model followed by manual verification and refinement. The dataset contains 1744 annotations across four categories: ‘Shrubs’, ‘Grasses’, ‘Legumes’, and ‘Others’. It includes diverse morphological variations and captures real-world challenges such as occlusion and lighting variability. This dataset serves as a benchmark for training object detection models in agricultural settings, facilitating the development of automated monitoring systems for precision agriculture. Such a mechanism could be incorporated into a mobile application, mounted on a drone, or embedded in an animal-worn device, enabling automated sampling and identification of the plant composition within a pasture. Full article

Background/Objectives: Centric relation (CR) is a reproducible mandibular reference position that plays a critical role in complex prosthodontic cases. With the advent of digital jaw-tracking devices, CR can now be recorded with greater precision through fully digital methods. This study aimed to compare patient-reported outcome measures (PROMs) for the recording of CR determined with conventional and digital techniques. Methods: Patients requiring occlusal rehabilitation due to bilateral loss of posterior support in the maxilla were included. Two different jaw relation recording techniques were applied: conventionally determined CR and digitally determined CR. The former was determined using bimanual manipulation, while the latter through multiple mandibular closure recordings performed with an anterior plateau using a jaw-tracking device. PROMs were assessed using Visual Analog Scale (VAS) to evaluate patient experience during jaw relation recording and comfort during restoration try-in. The recording time for both techniques was documented, and the correlation between recording time and VAS scores related to the recording procedure was analyzed. Statistical analyses were performed using the Wilcoxon signed-rank test and Spearman correlation analysis (α = 0.05). Results: Twelve patients were included. No statistically significant difference was found between the two methods in VAS scores assessing patient-reported comfort and experience. Recording time was significantly shorter for the recording of conventionally determined CR (p = 0.002). No statistically significant correlation was found between recording time and patient-reported experience for both techniques (p > 0.05). Conclusions: Despite the need for clinician experience and patient compliance, PROMs for digitally determined CR were comparable to those of conventionally determined CR. Full article

Upper extremity impairments often lead to reduced joint range of motion (ROM), making reliable assessment essential for rehabilitation planning. This study investigated the within-day and between-day reliability of the Microsoft Kinect V2 depth camera for active upper extremity ROM assessment in 30 healthy adults. Ten predefined shoulder and elbow movements were recorded, and joint angles were computed using a custom vector-based algorithm. Within-day reliability ranged from moderate to excellent (ICC: 0.754–0.953), while between-day reliability ranged from moderate to good (ICC: 0.654–0.881). Absolute reliability varies substantially across movements. The SEM% values ranged from 2.1% to 17.3% within-day and from 2.8% to 23.6% between-day. The between-day MDC values were particularly high for certain movements (e.g., >20° for shoulder extension and >50° for elbow flexion), indicating limited sensitivity to detect small clinical changes. Additionally, shoulder adduction could not be reliably analyzed in 36.7% of participants due to self-occlusion-related tracking instability, highlighting a practical limitation of the Kinect V2 for certain upper extremity movements. These findings suggest that Kinect V2-based ROM assessment demonstrates acceptable reliability for large-amplitude planar movements under controlled conditions but shows substantial limitations for rotational and occlusion-prone tasks. The device may be suitable for research or screening applications; however, caution is warranted when interpreting small changes in clinical settings. Full article

For decades, zirconia- and ceramic-based materials have dominated dental crown fabrication due to their durability and aesthetic appeal. However, a fundamental shift is occurring as polymeric alternatives emerge with notable advantages: better adhesive bonding, versatile aesthetics, lower costs, and a lighter weight. The advances in polymer chemistry and additive manufacturing have significantly impacted prosthodontics, allowing the rapid creation of highly customized, patient-specific restorations with a precision previously impossible (achieved through advanced Computer-Aided Design software and standardized 3D-printing equipment) with traditional methods. This review provides a detailed analysis of 3D-printed polymeric dental crowns from various angles. It explores the materials science behind different polymers, compares manufacturing methods, and evaluates the mechanical performance and biocompatibility. Despite the progress, polymeric materials still fall short of matching the mechanical properties of advanced ceramics, especially in compressive strength and wear resistance. Moreover, there is limited long-term clinical data over five to ten years. The lack of standardized testing protocols complicates cross-study comparisons, and the regulatory pathways for patient-specific 3D-printed devices are still developing, creating uncertainty for manufacturers and clinicians. The future prospective looks promising in many ways such as innovations like four-dimensional printing, where materials respond dynamically to environmental stimuli, which could enable crowns that adapt to changing oral conditions. Nanocomposites with functionalized nanoparticles might enhance mechanical properties while maintaining printability. AI-driven design optimization could automate and improve the crown morphology, occlusal contacts, and fit. Incorporating bioactive materials could turn crowns into active therapeutic devices that promote remineralization and combat bacterial colonization. This review summarizes the current knowledge, highlights the key gaps, and suggests steps toward establishing polymeric 3D-printed crowns as viable long-term alternatives capable of competing with or surpassing traditional ceramic options. Full article

Most 2D human pose estimation frameworks utilize static designs for multi-scale feature fusion, where information from various scales is integrated using fixed weights. A drawback of these approaches is that they often lead to localization biases in complex scenarios. This paper addresses the issues of multi-scale feature mismatch and joint localization biases in pose estimation. From the perspective of feature processing, multi-scale weights must be adapted to the size and position of joints, while joint predictions should adhere to human anatomical constraints. Existing methods lack effective dynamic adaptation, structural constraints, and bidirectional complementarity between high-level semantics and low-level details. They often experience localization biases in occluded scenarios, and the peaks of their heatmaps demonstrate insufficient consistency with the actual positions of the joints. Through theoretical analysis, we identify the causes of performance gaps and propose directions for narrowing them. We propose Bidirectional Multi-Scale Collaborative Pose Estimation (BiMS-Pose), a framework that introduces dynamic weights to adjust feature proportions, establishes bidirectional topological constraints for joint relationships, and integrates a bidirectional attention flow. The framework filters key information from three dimensions, adjusts filtering strategies in real time, and is enhanced by heatmap optimization to improve localization accuracy. Extensive experiments conducted on COCO, MPII, and our self-built Orchard Spraying Pose Dataset (OSPD) demonstrate the effectiveness of BiMS-Pose. In general scenarios, it achieves a significant 1.2 percentage-point increase in average precision (AP) on the COCO val2017 dataset compared to ViTPose while utilizing the same backbone. In agricultural orchard spraying scenarios, it effectively addresses interference factors such as changes in illumination, occlusion, and varying shooting distances, achieving 75.4% average precision (AP) and 90.7% percent of correct keypoints (PCKh@0.5) on the OSPD dataset. Additionally, it maintains an average frame rate of 18.3 FPS on embedded devices, effectively meeting the requirements for real-time monitoring. This highlights the model’s potential for precise, stable, and practical human pose estimation in both general and agricultural application scenarios. Full article