Search Results (159)

With the development of artificial intelligence, intelligent assessment methods have been applied in post-earthquake emergency rescue. These methods enable rapid and accurate identification and localization of earthquake-induced damage to ceilings in large public buildings, which often serve as emergency shelters. However, in practical applications, challenges remain: damage recognition accuracy is low when using wide-field distant shots, while close-up local shots are unsuitable for identifying panoramic regional damage. As a result, high-precision intelligent safety assessment of the entire ceiling area cannot be achieved. Therefore, this study proposes a panoramic image stitching method based on SIFT feature point detection and registration, optimized by the RANSAC algorithm, to generate high-resolution, wide-angle panoramic images of ceilings in large public buildings. The BRISQUE values of the stitched images range between 20 and 30, indicating good stitching quality. Subsequently, by integrating damage recognition and image stitching techniques, a safety assessment test was conducted on 227 stitched images of earthquake-induced ceiling damage captured in real scenes, using evaluation indicators such as damage type and severity quantification. The safety assessment achieved an overall accuracy of 98.7%, demonstrating the effectiveness of ceiling damage detection technology based on image stitching. This technology enables intelligent post-earthquake safety assessment of ceilings in large public buildings across the entire area. Full article

Background: Osteoporosis screening in dental practice is challenging because dual-energy X-ray absorptiometry is not easily applicable to jaw bones. Objective: This study aimed to evaluate the diagnostic performance of a logistic regression model combining intraoral bone mineral density (BMD) using DentalSCOPE with the panoramic mandibular cortical index (MCI) for osteoporosis screening. Methods: Among 104 patients included in the study, 83 who underwent both intraoral and panoramic radiography were retrospectively selected as a training cohort to develop a logistic regression model for osteoporosis prediction. The mean age was 52.4 years, and 65.1% were female. Intraoral radiographs were analyzed using DentalSCOPE^® (Media Co., Tokyo, Japan) to determine BMD in the alveolar region (al-BMD). On panoramic radiographs, experienced radiologists determined the MCI. An additional 21 patients (mean age 63.1 years; 81.0% female) were prospectively enrolled as an external validation cohort. The trained model was applied to both the training (internal) and external cohorts to evaluate its diagnostic performance, which was compared with that of intraoral or panoramic radiography, using receiver operating characteristic (ROC) analysis. Results: In the training cohort, areas under the ROC curve (AUCs) of al-BMD and MCI were 0.74 and 0.82, respectively, while the combined model showed improved performance with an AUC of 0.88. In the external validation cohort, the AUCs were 0.92 and 0.97 for al-BMD and MCI, respectively. The performance of the combined model improved with an area under the AUC of 1.00. Conclusions: DentalSCOPE-based al-BMD, particularly when combined with panoramic MCI, offers a reliable and practical approach for opportunistic osteoporosis screening in dental care. Full article

With the strict requirements of national policies on truck dimensions, axle loads, and weight limits, along with the implementation of tolls based on vehicle types, rapid and accurate identification of vehicle axle types has become essential for toll station management. To address the limitations of existing methods in distinguishing between drive and driven axles, complex equipment setup, and image evidence retention, this article proposes a panoramic image detection technology for vehicle chassis based on enhanced ORB and YOLOv11. A portable vehicle chassis image acquisition system, based on area array cameras, was developed for rapid on-site deployment within 20 min, eliminating the requirement for embedded installation. The FeatureBooster (FB) module was employed to optimize the ORB algorithm’s feature matching, and combined with keyframe technology to achieve high-quality panoramic image stitching. After fine-tuning the FB model on a domain-specific area scan dataset, the number of feature matches increased to 151 ± 18, substantially outperforming both the pre-trained FB model and the baseline ORB. Experimental results on axle type recognition using the YOLOv11 algorithm combined with ORB and FB features demonstrated that the integrated approach achieved superior performance. On the overall test set, the model attained an mAP@50 of 0.989 and an mAP@50:95 of 0.780, along with a precision (P) of 0.98 and a recall (R) of 0.99. In nighttime scenarios, it maintained an mAP@50 of 0.977 and an mAP@50:95 of 0.743, with precision and recall both consistently at 0.98 and 0.99, respectively. The field verification shows that the real-time and accuracy of the system can provide technical support for the axle type recognition of toll stations. Full article

Artificial Intelligence (AI) applications in sports dentistry have the potential to improve early detection and diagnosis. We aimed to validate the diagnostic performance of AI-assisted software in detecting dental caries, periodontitis, and tooth wear using panoramic radiographs in elite athletes. This cross-sectional validation study included secondary data from 114 elite athletes from the Sports Dentistry department at Egas Moniz Dental Clinic. The AI software’s performance was compared to clinically validated assessments. Dental caries and tooth wear were inspected clinically and confirmed radiographically. Periodontitis was registered through self-reports. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as the area under the curve and respective 95% confidence intervals. Inter-rater agreement was assessed using Cohen’s kappa statistic. The AI software showed high reproducibility, with kappa values of 0.82 for caries, 0.91 for periodontitis, 0.96 for periapical lesions, and 0.76 for tooth wear. Sensitivity was highest for periodontitis (1.00; AUC = 0.84), moderate for caries (0.74; AUC = 0.69), and lower for tooth wear (0.53; AUC = 0.68). Full agreement between AI and clinical reference was achieved in 86.0% of cases. The software generated a median of 3 AI-specific suggestions per case (range: 0–16). In 21.9% of cases, AI’s interpretation of periodontal level was deemed inadequate; among these, only 2 cases were clinically confirmed as periodontitis. Of the 34 false positives for periodontitis, 32.4% were misidentified by the AI. The AI-assisted software demonstrated substantial agreement with clinical diagnosis, particularly for periodontitis and caries. The relatively high false-positive rate for periodontitis and limited sensitivity for tooth wear underscore the need for cautious clinical integration, supervision, and further model refinements. However, this software did show overall adequate performance for application in Sports Dentistry. Full article

Background and Objectives: This study retrospectively investigated the impact of regenerative endodontic treatments (RET) on the healing of periapical lesions in young permanent molars with open apices. Our objective was to evaluate the relationship between treatment outcomes and changes in the fractal dimension (FD) of the periapical bone before and after RET. The study was conducted at the Department of Pediatric Dentistry, Necmettin Erbakan University between January 2020 and December 2024. Materials and Methods: We examined panoramic radiographs from systematically healthy patients aged 6–16 years who underwent RET in the posterior mandible between January 2020 and December 2024. Changes in periapical bone were assessed using fractal analysis before treatment and after a 6-month follow-up. Additionally, mental index (MI), mandibular cortical width (MCW), mental length (ML), and periapical index (PAI) values were evaluated. Radiographs were taken with a Planmeca ProOne^® device and analyzed using ImageJ v1.54 software. Results: Comparison of FD values between treated and contralateral tooth areas, as well as before and after RET, revealed an average FD value of 1.27 ± 0.05 after regeneration, increasing to 1.29 ± 0.27 at the 6-month follow-up. Significant increases were observed in MCW (p = 0.005/p = 0.049) and ML (p = 0.022/p = 0.001) in the 35–36 and 45–46 regions post-RET, though MI values showed no significant change. Importantly, PAI scores demonstrated significant improvement after RET. Conclusions: The findings suggest that RET is effective in promoting the healing of periapical lesions in young permanent molars. The observed increases in cortical width and improvements in PAI scores support the positive impact of this treatment on bone healing. Furthermore, FD analysis, when combined with radiomorphometric indices, could provide a valuable and objective tool for evaluating RET outcomes. Full article

Background/Objectives: Ischemic heart disease is typically characterized by chest pain that sometimes radiate to other areas, including the orofacial region. Atypical clinical presentation of cardiac disease risks leading to a delay in the diagnosis and treatment. Symptoms in the orofacial region may also lead to unnecessary dental interventions. The objective of this study was to assess occurrence of toothache, or other oral problems, that prompted a visit to a dental office prior to a first myocardial infarction. Methods: In 2010 until 2014, a total of 805 patients hospitalized for a first myocardial infarction and 805 controls matched for age, sex, and postal code area, were recruited to the case–control study PAROKRANK (Periodontitis and its relation to cardiovascular disease). In addition to medical and oral examinations that included panoramic radiography and blood sampling, all participants responded to a survey that covered questions related to oral habits and dental service use. The present study focused on responses to questions concerning the most recent visit to a dental office, specifically if toothache, chewing problem, and/or other problems with the teeth were present, whilst also taking endodontic variables into consideration. Results: Time since the most recent visit to a dental office ranged between 0–14 years, with a mean value of 1.08 years and no difference between patients and controls. A majority of responders (80.9%) gave the reason to be a routine dental examination. Toothache as the reason was reported by 146 (11.5%) respondents: 71 (10.9%) patients and 75 (12.1%) controls (p = 0.59). No difference was observed between patients and controls. Conclusions: Within the limitations of the present study design, seeking dental care for toothache was not associated with the risk of a subsequent first myocardial infarction. Full article

This research explores the environmental safety challenges faced by pocket parks in the context of urban aging within Chinese cities. It systematically analyzes visual elements that influence the elderly’s perception of environmental safety by applying interpretable machine learning techniques. By integrating panoramic image semantic segmentation and explainable AI models (e.g., SHAP and PDP), the study transforms subjective environmental perception into measurable indicators and constructs an environmental safety perception model using the LightGBM algorithm. Results indicate that sufficient pedestrian areas and moderate crowd activities significantly enhance safety perception among the elderly. Conversely, the presence of cars emerges as the most substantial adverse factor. Natural elements, such as vegetation and grass, exhibit nonlinear effects on safety perception, with an optimal threshold range identified. The research further elucidates the intricate synergies and constraints among visual elements, underscoring that the highest perceived safety arises from the synergistic combination of positive factors. This study deepens the understanding of environmental perception among the elderly and offers a data-driven framework and practical guidelines for urban planners and designers. It holds significant theoretical and practical implications for advancing the refined and human-centered renewal of urban public spaces. Full article

Objective: To evaluate the performance of vision transformer (ViT)-based deep learning models in the classification of open apex on panoramic radiographs (orthopantomograms (OPGs)) and compare their diagnostic accuracy with conventional convolutional neural network (CNN) architectures. Materials and Methods: OPGs were retrospectively collected and labeled by two observers based on apex closure status. Two ViT models (Base Patch16 and Patch32) and three CNN models (ResNet50, VGG19, and EfficientNetB0) were evaluated using eight classifiers (support vector machine (SVM), random forest (RF), XGBoost, logistic regression (LR), K-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), and multi-layer perceptron (MLP)). Performance metrics (accuracy, precision, recall, F1 score, and area under the curve (AUC)) were computed. Results: ViT Base Patch16 384 with MLP achieved the highest accuracy (0.8462 ± 0.0330) and AUC (0.914 ± 0.032). Although CNN models like EfficientNetB0 + MLP performed competitively (0.8334 ± 0.0479 accuracy), ViT models demonstrated more balanced and robust performance. Conclusions: ViT models outperformed CNNs in classifying open apex, suggesting their integration into dental radiologic decision support systems. Future studies should focus on multi-center and multimodal data to improve generalizability. Full article

Comprehensive evaluation of suburban landscape perception is essential for improving environmental quality and fostering integrated urban–rural development. Despite its importance, limited research has systematically extracted local visual features and analyzed influencing factors in suburban landscapes using multi-source data and machine learning. This study investigated Chongming District, a suburban area of Shanghai. Using Baidu Street View 360° panoramic images, local visual features were extracted through semantic segmentation of street view imagery, spatial multi-clustering, and random forest classification. A geographic detector model was employed to explore the relationships between landscape characteristics and their driving factors. The findings of the study indicate (1) significant spatial variations in the green visibility, sky openness, building density, road width, facility diversity, and enclosure integrity; (2) an intertwined spatial pattern of blue, green, and gray spaces; (3) the emergence of natural environment dimension factors as the primary drivers influencing the spatial configuration. In the suburban industrial dimension, the interaction between the GDP and commercial vitality exhibits the highest level of synergy. Based on these findings, targeted strategies are proposed to enhance the distinctive landscape features of Chongming Island. This research framework and methodology are specifically applied to Chongming District as a case study. Future studies should consider modifying the algorithms and index systems to better reflect other study areas, thereby ensuring the validity and precision of the results. Full article

Background: Dental caries arise from a multifactorial interplay between microbial dysbiosis, host immune responses, and enamel degradation visible on radiographs. Deep learning excels in image-based caries detection; however, integrative analyses that combine radiographic, microbiome, and transcriptomic data remain rare because public cohorts are seldom aligned. Objective: To determine whether three independent deep-learning pipelines—radiographic segmentation, microbiome regression, and transcriptome regression—can be reproducible implemented on non-aligned datasets, and to demonstrate the feasibility of estimating microbiome heritability in a matched twin cohort. Methods: (i) A U-Net with ResNet-18 encoder was trained on 100 annotated panoramic radiographs to generate a continuous caries-severity score from a predicted lesion area. (ii) Feed-forward neural networks (FNNs) were trained on supragingival 16S rRNA profiles (81 samples, 750 taxa) and gingival transcriptomes (247 samples, 54,675 probes) using randomly permuted severity scores as synthetic targets to stress-test preprocessing, training, and SHAP-based interpretability. (iii) In 49 monozygotic and 50 dizygotic twin pairs (n = 198), Bray–Curtis dissimilarity quantified microbial heritability, and an FNN was trained to predict recorded TotalCaries counts. Results: The U-Net achieved IoU = 0.564 (95% CI 0.535–0.594), precision = 0.624 (95% CI 0.583–0.667), recall = 0.877 (95% CI 0.827–0.918), and correlated with manual severity scores (r = 0.62, p < 0.01). The synthetic-target FNNs converged consistently but—as intended—showed no predictive power (R² ≈ −0.15 microbiome; −0.18 transcriptome). Twin analysis revealed greater microbiome similarity in monozygotic versus dizygotic pairs (0.475 ± 0.107 vs. 0.557 ± 0.117; p = 0.0005) and a modest correlation between salivary features and caries burden (r = 0.25). Conclusions: Modular deep-learning pipelines remain computationally robust and interpretable on non-aligned datasets; radiographic severity provides a transferable quantitative anchor. Twin-cohort findings confirm heritable patterns in the oral microbiome and outline a pathway toward future clinical translation once patient-matched multi-omics are available. This framework establishes a scalable, reproducible foundation for integrative caries research. Full article

Backgroud: The aim of this study was to create an accessible, simple and reliable method for assessing the relative density of bone tissue in dentistry based on the analysis of digital panoramic radiographs. Methods: Measurement of average gray values on orthopantomograms was carried out using ImageJ Version 1.54i software. To estimate the relative bone density, functions for selecting regions of interest (ROI), calculating the area of selection, and statistics of the selected area were used. Statistical characteristics of samples and testing of hypotheses using statistical criteria were performed using Microsoft Excel. Results: we found that when manually selecting the reference and comparison areas for areas without signs of pathological changes in bone tissue, the average standard deviation was 0.058, and the coefficient of variation was 0.055 ± 0.011%, which makes the choice of the jaw angle as a reference more preferable. The average relative bone density of the assessed defective areas to the jaw angle was 0.64 ± 0.11, and the average relative bone density of the areas without pathology to the jaw angle was 1.052 ± 0.058. Conclusions: a research protocol was developed and justified using the ImageJ software package, which establishes a strict procedure for quantitative assessment of relative bone density based on the results of digital panoramic radiography. The proposed protocol can be used to monitor the condition of bone tissue after all types of dental treatment over time. Full article

Background: This retrospective study aimed to assess the prevalence and distribution of common soft tissue ossifications and calcifications in the head and neck area, such as tonsilloliths, calcified lymph nodes, atherosclerotic plaques, stylohyoid ligament calcifications, and laryngeal cartilage calcifications, using panoramic radiographs (PRs) from a Turkish population. A secondary objective was to analyze these findings based on age and gender, ultimately seeking to enhance clinicians’ awareness of these incidental findings and their potential diagnostic significance. Methods: PRs of 1207 patients applying to the Department of Oral and Maxillofacial Radiology at Necmettin Erbakan University Faculty of Dentistry between 2021 and 2022 were reviewed. Out of these, 1193 images meeting quality criteria and showing distinct anatomical details were included. Patients with prior diagnosed bone metabolic disorders were excluded. Two radiologists independently assessed the images for the presence of soft tissue calcifications and ossifications. Inter-observer reliability was quantified using Cohen’s Kappa coefficient, which was found to be 0.78, indicating substantial agreement (95% CI: [0.72–0.83], p < 0.001). The calcifications and ossifications were categorized according to age, gender, and type. Data were analyzed employing descriptive statistical methods and Chi-square tests, with a significance level set at p < 0.05. Results: Soft tissue calcification or ossification was observed in 122 (10.22%) of the 1193 retrospectively evaluated PRs. The most common findings included stylohyoid ligament ossifications (n = 31), laryngeal cartilage calcifications (n = 28), tonsilloliths (n = 25), calcified atherosclerotic plaques (n = 18), and calcified lymph nodes (n = 18). Two antroliths were also identified. Arteriosclerosis, phleboliths, and sialoliths were not detected in this cohort. Although some types of calcification showed numerical variations across age groups and genders (e.g., higher prevalence of most anomalies in patients aged 31 years and older; more frequent laryngeal cartilage calcification in women and tonsilloliths in men), Chi-square analyses revealed no statistically significant association between the presence of these calcifications or ossifications and either age group (p = 0.284) or gender (p = 0.122). Conclusions: PRs serve as an effective initial screening instrument for identifying soft tissue calcifications within the head and neck region, owing to their widespread availability, cost-effectiveness, and minimal radiation exposure. The detection of such findings is of paramount importance, as they may indicate underlying systemic conditions necessitating further diagnostic evaluation. While clinicians should remain vigilant to these anomalies, definitive diagnosis typically requires supplementary imaging modalities such as cone-beam computed tomography (CBCT), ultrasound, or histopathological analysis. Full article

With the rapid growth of energy demand, the development of efficient energy-conversion technologies (e.g., water splitting, fuel cells, metal-air batteries, etc.) becomes an important way to circumvent the problems of fossil fuel depletion and environmental pollution, which motivates the pursuit of high-performance electrocatalysts with controllable compositions and morphologies. Among them, two-dimensional (2D) Pt-group metallic electrocatalysts show a series of distinctive architectural merits, including a high surface-to-volume ratio, numerous unsaturated metal atoms, an ameliorative electronic structure, and abundant electron/ion transfers channels, thus holding great potential in realizing good selectivity, rapid kinetics, and high efficiency for various energy-conversion devices. Considering that great progress on this topic has been made in recent years, here we present a panoramic review of recent advancements in 2D Pt-group metallic nanocrystals, which covers diverse synthetic methods, structural analysis, and their applications as electrode catalysts for various energy-conversion technologies. At the end, the paper also outlines the research challenges and future opportunities in this emerging area. Full article

Background: Radiographic evaluation of bone regeneration following maxillary sinus floor elevation commonly emphasizes volumetric gains. However, the qualitative microarchitecture of the regenerated bone, particularly when assessed via two-dimensional imaging modalities, such as panoramic radiographs, remains insufficiently explored. This study aimed to evaluate early trabecular changes in grafted maxillary sinus regions using fractal dimension, first-order statistics, and gray-level co-occurrence matrix analysis. Methods: This retrospective study included 150 patients who underwent maxillary sinus floor augmentation with bovine-derived xenohybrid grafts. Postoperative panoramic radiographs were analyzed at 6 months to assess early healing. Four standardized regions of interest representing grafted sinus floors and adjacent tuberosity regions were analyzed. Image processing and quantitative analyses were performed to extract fractal dimension (FD), first-order statistics (FOS), and gray-level co-occurrence matrix (GLCM) features (contrast, homogeneity, energy, correlation). Results: A total of 150 grafted sites and 150 control tuberosity sites were analyzed. Fractal dimension (FD) and contrast values were significantly lower in grafted areas than in native tuberosity bone (p < 0.001 for both), suggesting reduced trabecular complexity and less distinct transitions. In contrast, higher homogeneity (p < 0.001) and mean gray-level intensity values (p < 0.001) were observed in the grafted regions, reflecting a more uniform but immature trabecular pattern during the early healing phase. Energy and correlation values also differed significantly between groups (p < 0.001). No postoperative complications were reported, and resorbable collagen membranes appeared to support graft stability. Conclusions: Although the grafted sites demonstrated radiographic volume stability, their trabecular architecture remained immature at 6 months, implying that volumetric measurements alone may be insufficient to assess biological bone maturation. These results support the utility of advanced textural and fractal analysis in routine imaging to optimize clinical decision-making regarding implant placement timing in grafted sinuses. Full article

This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of the magnetosphere based on the Solar Wind Charge Exchange (SWCX) mechanism. However, several factors are expected to hinder future in-orbit observations, including the intrinsically low signal-to-noise ratio (SNR) of soft-X-ray emission, pronounced vignetting, and the non-uniform effective-area distribution of lobster-eye optics. These limitations could severely constrain the accurate interpretation of magnetospheric structures—especially the magnetopause boundary. To address these challenges, a boundary detection approach is developed that combines image calibration with denoising based on deep image prior (DIP). The method begins with calibration procedures to correct for vignetting and effective area variations in the SXI images, thereby restoring the accurate brightness distribution and improving spatial uniformity. Subsequently, a DIP-based denoising technique is introduced, which leverages the structural prior inherent in convolutional neural networks to suppress high-frequency noise without pretraining. This enhances the continuity and recognizability of boundary structures within the image. Experiments use ideal magnetospheric images generated from magnetohydrodynamic (MHD) simulations as reference data. The results demonstrate that the proposed method significantly improves the accuracy of magnetopause boundary identification under medium and high solar wind number density conditions (N = 10–20 cm⁻³). The extracted boundary curves consistently achieve a normalized mean squared error (NMSE) below 0.05 compared to the reference models. Additionally, the DIP-processed images show notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), indicating enhanced image quality and structural fidelity. This method provides adequate technical support for the precise extraction of magnetopause boundary structures in soft X-ray observations and holds substantial scientific and practical value. Full article