Search Results (353)

Background/Objectives: Gastric cancer remains a leading cause of cancer-related mortality, with over 50% of cases diagnosed at a locally advanced or metastatic stage. High-quality surgical resection within the embryological mesogastric layer is critical for achieving optimal oncological outcomes but is often complicated by anatomical distortion in advanced tumors. This study aimed to develop and validate a system of topographic and anatomical navigation landmarks for embryologically guided laparoscopic gastrectomy, leveraging 3D modeling to enhance precision and safety. Methods: A single-center study was conducted, analyzing 78 patients undergoing emergency laparoscopic gastrectomy for locally advanced gastric cancer. Preoperative 3D models were generated from CT data annotations to map the stomach, tumor, vascular structures, and mesogastric adipose tissue. Thirty biomodels were used to refine dissection techniques. Surgical procedures adhered to embryological principles, with lymphadenectomy guided by predefined landmarks. Histopathological validation assessed resection margins and tumor infiltration in resected specimens. Statistical analysis compared outcomes between patients with and without 3D planning. Results: The 3D models demonstrated 100% concordance with intraoperative vascular anatomy. Radiologically dense adipose tissue, resected as potentially tumor-infiltrated, showed histopathological invasion in 74% of cases. R0 resection was achieved in 74.4% of patients. Operative time decreased from 300 to 250 min after technical optimization, with a 7.7% conversion rate (primarily due to vascular injury or tumor fixation). Postoperative mortality was 5.1%, attributed to comorbidities. Patients with 3D planning had significantly higher lymph node yields (p < 0.00001) and R0 rates (p = 0.045). Conclusions: The integration of embryologically based topographic landmarks and 3D navigation improves the safety and standardization of laparoscopic gastrectomy for locally advanced gastric cancer. This approach enhances oncological radicality, reduces operative time, and mitigates risks in anatomically distorted fields. Further validation in larger cohorts is warranted. Full article

Background: Geographic information systems (GIS) are a promising tool for mapping vaccination coverage and identifying missed communities, yet their use in low- and middle-income countries (LMICs) remains limited. In settings without standardized addresses such as schools or outreach sites, innovative methods are needed to collect and analyse spatial data. Schools offer a unique platform for identifying under-vaccinated children missed by routine or campaign efforts. Methods: During a pilot school vaccination screening program in Zambia, GIS reference maps of health facility catchment areas were developed from hand-drawn sketch maps, catchment area shapefiles, and coordinates of prominent landmarks. These maps were iteratively refined with input from local health staff. In caregiver interviews, data collectors used the maps to identify the child’s zone of residence within the health facility catchment area. Vaccination status was extracted from paper registries used during screening. Geographic heat maps were generated in ArcGIS to visualize under-vaccination by zone. Results: Of 535 children screened across 25 zones, 29% were under-vaccinated. Under-vaccination varied by zone, with clusters of missed children identified, for example, 50% of children in Kabushi Zone 6 were under-vaccinated, compared with much lower rates elsewhere. Conclusions: Pairing school-based vaccination checks with GIS mapping offers a scalable approach to identifying missed communities in LMICs. This method enables spatial analysis without household visits, supporting targeted immunization planning where traditional data systems fall short. However, because the study was limited to children enrolled in five purposively selected schools, out-of-school children and those in other schools were not represented. This selection bias may underestimate the true extent of under-vaccination, and future evaluations should incorporate broader and more representative populations. Full article

Background: Liver resection remains the gold standard treatment for colorectal cancer (CRC) liver metastases, while stereotactic body radiotherapy (SBRT) offers an alternative for patients with unresectable metastases. However, the precise indications for SBRT, optimal radiation doses, and treatment regimens have yet to be definitively established. Methods: A total of 91 patients with 152 lesions underwent SBRT, receiving a total dose ranging from 40 to 60 Gy delivered in 4–5 fractions per lesion, with a median dose of 50 Gy. Results: The three-year local control (LC) and overall survival (OS) rates were 62.6% and 45.1%, respectively. No cases of Grade ≥ 3 toxicity were observed. Factors negatively affecting LC included metastasis diameter ≥ 2.7 cm and number of metastases ≥ 3, with hazard ratios (HR) of 2.73 and 2.24, respectively. A biologically effective dose (BED) of ≥137.7 Gy was associated with a significant improvement in local control (LC) (HR 0.25), a finding that was also confirmed by the inverse probability of treatment weighting (IPTW) analysis. Significant predictors for poorer OS included RAS gene mutations, metastasis diameter ≥ 2.6 cm, and synchronous metastases, with HRs of 2.27, 2.03, and 2.11, respectively. Landmark analysis demonstrated that local recurrence within 12 months after SBRT significantly reduced OS (HR 2.68). Conclusions: SBRT is a safe and effective method for achieving local control of CRC liver oligometastases. Further research is warranted to optimize treatment protocols and refine patient selection criteria. Full article

This study aims to systematically reveal, from the perspective of organizational scale, the differences between large and small architectural design organizations in China in terms of characteristics of production activities, technological capabilities and innovation levels, resource integration capabilities, and client groups, and to quantify the priority order of clients’ attention to architectural design products, thereby providing a reference for industry structure optimization and strategic decision making. This research combines case analysis and comparative study to construct a four-dimensional comparative framework. The results show that large design organizations, leveraging their advantages in technological research and development as well as resource integration, focus on large-scale complex projects, technology-driven projects, cultural landmark projects, and multi-stakeholder collaborative projects, primarily serving government agencies and large enterprises. In contrast, small design organizations excel in flexibility, concentrating on small-scale simple projects, specialized niche projects, localized projects, and short-cycle, low-budget projects, serving individual owners and small businesses. Furthermore, this study adopts the Analytic Hierarchy Process (AHP) to establish an evaluation model. Twenty experts from architectural design organizations, construction organizations, and research institutions were invited to score the survey questionnaires, and quantitative weight analysis was performed. The research findings provide support for the optimization of the industry. Full article

Background/Objectives: Current bone-based landmark approaches have shown variable accuracy and poor reproducibility. We validated a two-point “tuberculum sellae–anterior clinoid process” (TS–ACP) line traced on routine 3D-computed tomography angiography (CTA) for predicting distal dural ring (DDR) position and quantified the interobserver agreement. Methods: We retrospectively reviewed data from 85 patients (87 aneurysms) who were treated via clipping between June 2012 and December 2024. Two blinded neurosurgeons classified each aneurysm as extradural, intradural, or straddling the TS–ACP line. The intraoperative DDR inspection served as the reference standard. Diagnostic accuracy, χ² statistics, and Cohen’s κ were calculated. Results: The TS–ACP line landmarks were identifiable in all cases. The TS–ACP line classification correlated strongly with operative findings (χ² = 138.3, p = 6.4 × 10⁻²⁹). The overall accuracy was 89.7% (78/87), and sensitivity and specificity for identifying intradural aneurysms were 94% and 82%, respectively. The interobserver agreement was substantial (κ = 0.78). Nine aneurysms were misclassified, including four cavernous-sinus lesions that partially crossed the DDR. Retrospective fusion using constructive interference in steady-state magnetic resonance imaging corrected these errors. Conclusions: The TS–ACP line represents a rapid, reproducible tool that reliably localizes the DDR on standard 3D-CTA, showing higher accuracy than previously reported single-landmark techniques. Its high accuracy and substantial inter-observer concordance support incorporation into routine preoperative assessments. Because the method depends on only two easily detectable bony points, it is well-suited for automated implementation, offering a practical pathway toward artificial intelligence-assisted stratification of paraclinoid aneurysms. Full article

Background and Objectives: Pelvic organ prolapse (POP) is a complex condition affecting the pelvic floor, often requiring imaging for accurate diagnosis and treatment planning. Artificial intelligence (AI), particularly deep learning (DL), is emerging as a powerful tool in medical imaging. This scoping review aims to synthesize current evidence on the use of AI in the imaging-based diagnosis and anatomical evaluation of POP. Materials and Methods: Following the PRISMA-ScR guidelines, a comprehensive search was conducted in PubMed, Scopus, and Web of Science for studies published between January 2020 and April 2025. Studies were included if they applied AI methodologies, such as convolutional neural networks (CNNs), vision transformers (ViTs), or hybrid models, to diagnostic imaging modalities such as ultrasound and magnetic resonance imaging (MRI) to women with POP. Results: Eight studies met the inclusion criteria. In these studies, AI technologies were applied to 2D/3D ultrasound and static or stress MRI for segmentation, anatomical landmark localization, and prolapse classification. CNNs were the most commonly used models, often combined with transfer learning. Some studies used hybrid models of ViTs, demonstrating high diagnostic accuracy. However, all studies relied on internal datasets, with limited model interpretability and no external validation. Moreover, clinical deployment and outcome assessments remain underexplored. Conclusions: AI shows promise in enhancing POP diagnosis through improved image analysis, but current applications are largely exploratory. Future work should prioritize external validation, standardization, explainable AI, and real-world implementation to bridge the gap between experimental models and clinical utility. Full article

This paper presents a bionic extended Kalman filter (EKF) state estimation algorithm for agricultural planters, inspired by the bionic mechanism of migratory birds navigating in complex environments, where migratory birds achieve precise localization behaviors by fusing multi-sensory information (e.g., geomagnetic field, visual landmarks, and somatosensory balance). The algorithm mimics the migratory bird’s ability to integrate multimodal information by fusing laser SLAM, inertial measurement unit (IMU), and GPS data to estimate the position, velocity, and attitude of the planter in real time. Adopting a nonlinear processing approach, the EKF effectively handles nonlinear dynamic characteristics in complex terrain, similar to the adaptive response of a biological nervous system to environmental perturbations. The algorithm demonstrates bio-inspired robustness through the derivation of the nonlinear dynamic teaching model and measurement model and is able to provide high-precision state estimation in complex environments such as mountainous or hilly terrain. Simulation results show that the algorithm significantly improves the navigation accuracy of the planter in unstructured environments. A new method of bio-inspired adaptive state estimation is provided. Full article

Background/Objectives: The mandibular lingula (ML) is a small bony projection on the medial surface of the ramus and serves as the first reference point identified during sagittal split ramus osteotomy (SSRO) or inferior alveolar nerve block (IANB). Anatomical variations in the mandibular ramus have been shown to exist across different populations. Understanding these population-specific differences enhances both clinical safety and diagnostic precision. However, there is a paucity of anthropological data amongst the Mongoloid population, especially in Southeast Asia. Hence, this study aimed to investigate the (i) distance of the lingula to different mandibular anatomical landmarks and its localization, (ii) lingula shape, and (iii) differences between gender and the sides of the mandible amongst the local ethnic groups. Methods: This retrospective cross-sectional study consisted of 206 cone-beam computed tomography (CBCT) images of 77 males and 129 females (mean age 33), with a total of 412 hemimandibles. Measurements were performed on three-dimensionally reconstructed CBCT images. Results: The most common shape was the truncated type. The distance of the lingula to the anterior (LiA), posterior (LiP), superior (LiS), and inferior (LiI) borders of mandible were 17.84 (2.25) mm, 14.46 (3.44) mm, 17.73 (3.00) mm, and 27.05 (4.40) mm, respectively. No significant difference exists between the sides of the mandible. Sexual dimorphism existed for all lingula measurements except LiA. Indians have smaller rami with more anteriorly and inferiorly placed ML than Malay and Chinese. The majority of ML was located 8.55 mm above the occlusal plane. Conclusions: This study provides information on the ML and its surrounding ramus structure in the local population. Such variations must be accounted for in SSRO and IANB. Full article

Drug-coated balloons (DCBs) have transformed percutaneous coronary intervention (PCI) by delivering antiproliferative drugs directly to the arterial wall, offering a stent-less approach that mitigates the risks associated with permanent metallic implants. Initially developed for in-stent restenosis (ISR), DCBs have demonstrated robust efficacy in reducing neointimal hyperplasia and target lesion revascularization (TLR) rates across diverse coronary lesions, including small vessel disease (SVD), de novo lesions, and complex anatomies such as bifurcation lesions. Paclitaxel-coated balloons have long been the cornerstone of DCB therapy due to their established clinical outcomes, but sirolimus-coated balloons are emerging as a promising alternative with potentially superior safety profiles and sustained drug release. The pharmacological mechanism of DCBs relies on rapid drug transfer during brief balloon inflation, achieving high local concentrations without residual foreign material. Landmark trials, such as BASKET-SMALL 2, RESTORE SVD, and AGENT IDE, have demonstrated comparable or non-inferior outcomes of DCBs versus drug-eluting stents (DESs) in specific settings, with lower rates of stent thrombosis and shorter dual antiplatelet therapy (DAPT) requirements. Despite these advances, challenges persist, including optimizing drug formulations, ensuring uniform delivery, and addressing calcified lesions. Ongoing research into novel coatings, dual–drug systems, and artificial intelligence (AI)-guided interventions is poised to redefine PCI strategies. This review provides a comprehensive analysis of drug-coated balloon (DCB) angioplasty, not limited to specific clinical scenarios such as in-stent restenosis, small vessel disease, or bifurcation lesions, highlighting their transformative role in coronary artery disease (CAD) management. Instead, it addresses the full spectrum of pharmacological principles, mechanisms of action, clinical indications, comparative efficacy across various coronary artery disease contexts, and future directions of DCBs. Full article

Vision-based navigation is a common solution for the critical challenge of GPS-denied Unmanned Aerial Vehicle (UAV) operation, but a research gap remains in the autonomous discovery of robust landmarks from aerial survey imagery needed for such systems. In this work, we propose a framework to fill this gap by identifying visually distinctive urban buildings from aerial survey imagery and curating them into a landmark database for GPS-free UAV localization. The proposed framework constructs semantically rich embeddings using intermediate layers from a pre-trained YOLOv11n-seg segmentation network. This novel technique requires no additional training. An unsupervised landmark selection strategy, based on the Isolation Forest algorithm, then identifies objects with statistically unique embeddings. Experimental validation on the VPAIR aerial-to-aerial benchmark shows that the proposed max-pooled embeddings, assembled from selected layers, significantly improve retrieval performance. The top-1 retrieval accuracy for landmarks more than doubled compared to typical buildings (0.53 vs. 0.31), and a Recall@5 of 0.70 is achieved for landmarks. Overall, this study demonstrates that unsupervised outlier selection in a carefully constructed embedding space yields a highly discriminative, computation-friendly set of landmarks suitable for real-time, robust UAV navigation. Full article

The treatment landscape of metastatic hormone-sensitive prostate cancer (mHSPC) has transformed significantly with the advent of triplet therapy involving androgen deprivation therapy (ADT), docetaxel, and androgen receptor signalling inhibitors (ARSIs). While clinical guidelines increasingly support early intensification, real-world practice remains challenged by patient heterogeneity, evolving evidence, and limited consensus on treatment sequencing. This narrative review integrates evidence from landmark trials, clinical guidelines, and expert insights from oncologists managing mHSPC in India. Findings affirm that triplet therapy, particularly with darolutamide, improves survival in high-volume disease and underscores the need for personalized treatment based on disease burden, comorbidities, and genomic profiles. The review also highlights gaps in real-world data, sequencing strategies, and biomarker-driven therapy, reinforcing the need for precision medicine and locally relevant evidence to guide treatment. Ultimately, optimizing mHSPC management requires harmonizing guideline-based approaches with individualized, real-world decision making to improve patient outcomes. Full article

Traditional acupuncture relies on the precise selection of acupuncture points to adjust Qi flow along meridians. Traditionally, acupuncture points are localized using cun (or body-relative cun) as a proportional measurement. However, locating specific points can be challenging, even for experienced practitioners. This study aims to enhance the accuracy and efficiency of acupuncture point localization by introducing an augmented reality (AR) navigation system utilizing AR glasses (Magic Leap One). The system employs a Six-Point Landmark-Based AR Registration method to overlay an acupuncture point model onto a patient’s head without the need for external markers. Methods included testing with traditional Chinese medicine students, measuring positional errors, and evaluating stability. Results demonstrated an average error of 5.01 ± 2.64 mm, which is well within the therapeutic range of 2 cun (about 5 cm), with minimal drift during stability tests. This AR system provides an accurate and intuitive tool for practitioners and learners, reducing variability in acupuncture point selection and offering promise for broader clinical applications. Full article

Localization of mobile robots is crucial for deploying robots in real-world applications such as search and rescue missions. This work aims to develop an accurate localization system applicable to swarm robots equipped only with low-cost monocular vision sensors and visual markers. The system is designed to operate in fully open spaces, without landmarks or support from positioning infrastructures. To achieve this, we propose a localization method based on equilateral triangular formations. By leveraging the geometric properties of equilateral triangles, the accurate two-dimensional position of each participating robot is estimated using one-dimensional lateral distance information between robots, which can be reliably and accurately obtained with a low-cost monocular vision sensor. Experimental and simulation results demonstrate that, as travel time increases, the positioning error of the proposed method becomes significantly smaller than that of a conventional dead-reckoning system, another low-cost localization approach applicable to open environments. Full article

To overcome the limitations of current train positioning systems, including low positioning accuracy and heavy reliance on track transponders or GNSS signals, this paper proposes a novel LiDAR-inertial and visual landmark fusion framework. Firstly, an IMU preintegration factor considering the Earth’s rotation and a LiDAR-inertial odometry factor accounting for degenerate states are constructed to adapt to railway train operating environments. Subsequently, a lightweight network based on YOLO improvement is used for recognizing reflective kilometer posts, while PaddleOCR extracts numerical codes. High-precision vertex coordinates of kilometer posts are obtained by jointly using LiDAR point cloud and an image detection box. Next, a kilometer post factor is constructed, and multi-source information is optimized within a factor graph framework. Finally, onboard experiments conducted on real railway vehicles demonstrate high-precision landmark detection at 35 FPS with 94.8% average precision. The proposed method delivers robust positioning within 5 m RMSE accuracy for high-speed, long-distance train travel, establishing a novel framework for intelligent railway development. Full article

The analysis of the spatial location of tourists is essential for effective tourism management. This study explores the potential effects of large language models (LLMs) on urban travel planning. Despite growing academic interest in LLMs, empirical research on their specific impact on urban tourist locations remains limited, even though these models may significantly affect tourist behavior and spatial dynamics. This article compares the location of heritage sites in the city of Barcelona that are traditionally visited by tourists (as identified through Instagram) with those recommended by ChatGPT. The results show that ChatGPT tends to recommend a much smaller and more spatially concentrated number of tourist attractions than those shared on Instagram. The findings indicate that ChatGPT reinforces mainstream representations of cities by prioritizing well-known landmarks, potentially overlooking emerging or local attractions. This simplification can lead to tourist overcrowding and the marginalization of less-visited areas. Likewise, it may entail new needs for the management of urban spaces. Urban planners and tourism managers may need to intervene to redistribute tourist flows in a context where various models of tourist behavior will coexist. Full article