Search Results (21,921)

This paper presents the PRONOBIS project, an ultrasound-only, robotically assisted, deep learning-based system for prostate scanning and biopsy treatment planning. The proposed system addresses the challenges of precise prostate segmentation, reconstruction and inter-operator variability by performing fully automated prostate scanning, real-time CNN-transformer-based image processing, 3D prostate reconstruction, and biopsy needle position planning. Fully automated prostate scanning is achieved by using a robotic arm equipped with an ultrasound system. Real-time ultrasound image processing utilizes state-of-the-art deep learning algorithms with intelligent post-processing techniques for precise prostate segmentation. To create a high-quality prostate segmentation dataset, this paper proposes a deep learning-based medical annotation platform, MedAP. For precise segmentation of the entire prostate sweep, DAF3D and MicroSegNet models are evaluated, and additional image post-processing methods are proposed. Three-dimensional visualization and prostate reconstruction are performed by utilizing the segmentation results and robotic positional data, enabling robust, user-friendly biopsy treatment planning. The real-time sweep scanning and segmentation operate at 30 Hz, which enable complete scan in 15 to 20 s, depending on the size of the prostate. The system is evaluated on prostate phantoms by reconstructing the sweep and by performing dimensional analysis, which indicates 92% and 98% volumetric accuracy on the tested phantoms. Three-dimansional prostate reconstruction takes approximately 3 s and enables fast and detailed insight for precise biopsy needle position planning. Full article

Background/Objectives: Elevated troponin levels are widely recognized as key biomarkers of myocardial injury and are frequently used in clinical decision making. However, not all instances of troponin elevation indicate true cardiac damage. In some cases, biochemical or immunological interferences may lead to false-positive results. These situations may lead to unnecessary diagnostic interventions and clinical uncertainty, ultimately impacting patient management negatively. This study aims to investigate the underlying mechanisms of false-positive troponin elevation in pediatric patients, focusing on factors such as macrotroponin formation, autoantibodies, and heterophile antibody interference. Methods: This retrospective study analyzed data from 13 pediatric patients who presented with elevated cardiac troponin levels between 2017 and 2024. Clinical evaluations included transthoracic echocardiography (TTE), electrocardiography (ECG), coronary computed tomography angiography (CTA), cardiac magnetic resonance imaging (MRI), and rheumatologic testing. Laboratory findings included measurements of cardiac troponins (cTnI and hs-cTnT) and pro-BNP levels. Results: Among 70 patients evaluated for elevated troponin levels, 13 (18.6%) were determined to have no identifiable cardiac etiology. The median age of these 13 patients was 13.0 years (range: 9–16), with 53.8% being female. The most common presenting complaints were chest pain (53.8%) and palpitations (30.8%). TTE findings were normal in 61.5% of the patients, and all patients had normal coronary CTA and cardiac MRI findings. Although initial troponin I levels were elevated in all cases, persistent positivity was observed up to 12 months. Median cTnI levels were 1.00 ng/mL (range: 0.33–7.19) at week 1 and 0.731 ng/mL (range: 0.175–4.56) at month 12. PEG precipitation testing identified macrotroponin in three patients (23.1%). No etiological explanation could be identified in 10 cases (76.9%), which were considered idiopathic. All patients had negative results for heterophile antibody and rheumatologic tests. Conclusions: When interpreting elevated troponin levels in children, biochemical interferences—especially macrotroponin—should not be overlooked. This study emphasizes the diagnostic uncertainty associated with non-cardiac troponin elevation. To better guide clinical practice and clarify false positivity rates, larger, multicenter prospective studies are needed. Full article

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

Background and Objectives: During pandemics, emergency departments face the challenge of managing critically ill patients with limited resources. Point-of-Care Ultrasound (POCUS) has emerged as a crucial diagnostic tool in such scenarios. This study aimed to develop a standardized POCUS protocol using expert consensus via a modified Delphi survey to guide physicians in managing these patients more effectively. Materials and Methods: A committee of emergency imaging experts and board-certified emergency physicians identified essential elements of POCUS in the treatment of patients under investigation (PUI) with shock, sepsis, or other life-threatening diseases. A modified Delphi survey was conducted among 39 emergency imaging experts who were members of the Korean Society of Emergency Medicine. The survey included three rounds of expert feedback and revisions, leading to the development of a POCUS protocol for critically ill patients during a pandemic. Results: The developed POCUS protocol emphasizes the use of POCUS-echocardiography and POCUS-lung ultrasound for the evaluation of cardiac and respiratory function, respectively. The protocol also provides guidance on when to consider additional tests or imaging based on POCUS findings. The Delphi survey results indicated general consensus on the inclusion of POCUS-echocardiography and POCUS-lung ultrasound within the protocol, although there were some disagreements regarding specific elements. Conclusions: Effective clinical practice aids emergency physicians in determining appropriate POCUS strategies for differential diagnosis between life-threatening diseases. Future studies should investigate the effectiveness and feasibility of the protocol in actual clinical scenarios, including its impact on patient outcomes, resource utilization, and workflow efficiency in emergency departments. Full article

Introduction: Traumatic brain injury (TBI) is a leading cause of morbidity worldwide, with the Glasgow Coma Scale (GCS) serving as a tool to measure injury severity. This study aimed to investigate the relationship between GCS admission scores and various socio-demographic, clinical, injury-related, and hospital-related variables in patients with TBI across two tertiary care centers in Eastern Europe, a region that remains underrepresented in the literature. Methods: A retrospective observational study was conducted using data from 119 TBI patients admitted between March 2020 and June 2023 at Cluj County Emergency Hospital (Romania) and Saint Vincent Hospital (Poland). GCS scores were analyzed as both categorical and continuous variables. Statistical analyses included Wilcoxon and Kruskal–Wallis tests for group comparisons and Spearman correlations for continuous variables. Results: Most patients included suffered a mild TBI (GCS score between 13 and 15). There were significant associations between GCS scores and post-traumatic amnesia (p < 0.05), discharge status (p < 0.01), discharge destination (p < 0.01), and education level (p < 0.01). GCS scores at admission were linked to survival, absence of post-traumatic amnesia, higher education levels, and home discharge. No significant differences observed across sex, residence, employment status, injury type, cause, or mechanism of injury. A weak but significant negative correlation was observed between GCS and length of hospital stay (rho = −0.229, p > 0.05), while age showed a non-significant correlation. Conclusions: The GCS score at admission is significantly associated with various clinical and socio-demographic outcomes in TBI patients, supporting the utility of the GCS score as a prognostic tool. The predominance of mild cases and the absence of radiological data, such as cerebral contusions or epidural or subdural hematomas, limit the generalizability of the findings. Further studies with larger samples and comprehensive imaging data are necessary to validate these findings. Full article

Face recognition (FR) in unconstrained conditions remains an open research topic and an ongoing challenge. The facial images exhibit diverse expressions, occlusions, variations in illumination, and heterogeneous backgrounds. This work aims to produce an accurate and robust system for enhanced Security and Surveillance. A parallel hybrid deep learning model for feature extraction and classification is proposed. An ensemble of three parallel extraction layer models learns the best representative features using CNN and RNN. 2D LBP and 3D Mesh LBP are computed on face images to extract image features as input to two RNNs. A stacked autoencoder (SAE) merged the feature vectors extracted from the three CNN-RNN parallel layers. We tested the designed 2D/3D CNN-RNN framework on four standard datasets. We achieved an accuracy of $98.9\%$. The hybrid deep learning model significantly improves FR against similar state-of-the-art methods. The proposed model was also tested on an unconstrained conditions human crowd dataset, and the results were very promising with an accuracy of $95\%$. Furthermore, our model shows an 11.5% improvement over similar hybrid CNN-RNN architectures, proving its robustness in complex environments where the face can undergo different transformations. Full article

As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications. Full article

In the last two decades, there has been a considerable surge in the development of artificial intelligence. Imaging is most frequently employed for the diagnostic evaluation of patients, as it is regarded as one of the most precise methods for identifying the presence of a disease. However, a study indicates that approximately 800,000 individuals in the USA die or incur permanent disability because of misdiagnosis. The present study is based on the use of computer-aided diagnosis of colorectal cancer. The objective of this study is to develop a practical, low-cost, AI-based decision-support tool that integrates clinical test data (blood/stool) and, if needed, colonoscopy images to help reduce misdiagnosis and improve early detection of colorectal cancer for clinicians. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) are utilized in conjunction with a graphical user interface (GUI), which caters to individuals lacking programming expertise. The performance of the artificial neural network (ANN) is measured using the mean squared error (MSE) metric, and the obtained performance is 7.38. For CNN, two distinct cases are under consideration: one with two outputs and one with three outputs. The precision of the models is 97.2% for RGB and 96.7% for grayscale, respectively, in the first instance, and 83% for RGB and 82% for grayscale in the second instance. However, using a pretrained network yielded superior performance with 99.5% for 2-output models and 93% for 3-output models. The GUI is composed of two panels, with the best ANN model and the best CNN model being utilized in each. The primary function of the tool is to assist medical personnel in reducing the time required to make decisions and the probability of misdiagnosis. Full article

To address the limitations of traditional tunnel structural plane modeling—such as low automation, insufficient smoothness, and poor adaptability to real construction environments—this study proposes a novel three-dimensional (3D) modeling framework based on B-spline interpolation combined with deep learning. The method first employs YOLOv5 for rapid detection of structural regions and DeepLabV3+ for precise boundary segmentation, followed by skeleton extraction and coordinate transformation to obtain spatial structural traces. Finally, B-spline interpolation is applied across multiple tunnel sections to construct continuous 3D surfaces. In model training and testing, the segmentation network achieved an F1 score of 94.01%, and the final modeling accuracy demonstrated a mean relative error (MRE) below 2.5%, confirming the reliability of the geometric reconstruction. Additionally, the proposed method was applied to excavation face images from the Paiyashan Tunnel, where multiple structural surfaces were successfully reconstructed in 3D, validating the approach’s applicability and robustness in real geological conditions. Compared to traditional triangulated or linear surface methods, the proposed approach achieves higher smoothness, better geological continuity, and improved automation, making it suitable for real-world geotechnical applications. Full article

Background/Objectives: The accurate identification of insect bites from images of skin is daunting due to the fine gradations among diverse bite types, variability in human skin response, and inconsistencies in image quality. Methods: For this work, we introduce DeepBiteNet, a new ensemble-based deep learning model designed to perform robust multiclass classification of insect bites from RGB images. Our model aggregates three semantically diverse convolutional neural networks—DenseNet121, EfficientNet-B0, and MobileNetV3-Small—using a stacked meta-classifier designed to aggregate their predicted outcomes into an integrated, discriminatively strong output. Our technique balances heterogeneous feature representation with suppression of individual model biases. Our model was trained and evaluated on a hand-collected set of 1932 labeled images representing eight classes, consisting of common bites such as mosquito, flea, and tick bites, and unaffected skin. Our domain-specific augmentation pipeline imputed practical variability in lighting, occlusion, and skin tone, thereby boosting generalizability. Results: Our model, DeepBiteNet, achieved a training accuracy of 89.7%, validation accuracy of 85.1%, and test accuracy of 84.6%, and surpassed fifteen benchmark CNN architectures on all key indicators, viz., precision (0.880), recall (0.870), and F1-score (0.875). Our model, optimized for mobile deployment with quantization and TensorFlow Lite, enables rapid on-client computation and eliminates reliance on cloud-based processing. Conclusions: Our work shows how ensemble learning, when carefully designed and combined with realistic data augmentation, can boost the reliability and usability of automatic insect bite diagnosis. Our model, DeepBiteNet, forms a promising foundation for future integration with mobile health (mHealth) solutions and may complement early diagnosis and triage in dermatologically underserved regions. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article

Background/Objectives: Muscle dysmorphia (MD), a subtype of body dysmorphic disorder, is prevalent among males who engage in the non-medical use of anabolic–androgenic steroids (AASs) and performance-enhancing drugs (PEDs). These individuals often experience severe psychopathology, including mood instability, compulsivity, and a distorted body image. Despite its clinical severity, no randomized controlled trials (RCTs) have evaluated structured psychological treatments in this subgroup. This study aimed to assess the efficacy of a manualized cognitive behavioral therapy (CBT) protocol in reducing MD symptoms and associated psychological distress among male steroid users. Results: Participants in the CBT group showed significant reductions in MD symptoms from the baseline to post-treatment (MDDI: p < 0.001, d = 1.12), with gains sustained at follow-up. Large effect sizes were also observed in secondary outcomes including depressive symptoms (PHQ-9: d = 0.98), psychological distress (K10: d = 0.93), disordered eating (EDE-Q: d = 0.74), and exercise addiction (EAI: d = 1.07). No significant changes were observed in the control group. Significant group × time interactions were found for all outcomes (all p < 0.01), indicating CBT’s specific efficacy. Discussion: This study provides the first RCT evidence that CBT significantly reduces both core MD symptoms and steroid-related psychopathology in men engaged in AAS/PED misuse. Improvements extended to mood, body image perception, and compulsive exercise behaviors. These findings support CBT’s transdiagnostic applicability in addressing both the cognitive–behavioral and affective dimensions of MD. Materials and Methods: In this parallel-group, open-label RCT, 59 male gym-goers with DSM-5-TR diagnoses of MD and a history of AAS/PED use were randomized to either a 12-week CBT intervention (n = 30) or a waitlist control group (n = 29). CBT sessions were delivered weekly online and targeted distorted muscularity beliefs, compulsive behaviors, and emotional dysregulation. Primary and secondary outcomes—Muscle Dysmorphic Disorder Inventory (MDDI), PHQ-9, K10, EDE-Q, EAI, and BIG—were assessed at the baseline, post-treatment, and 3-month follow-up. A repeated-measures ANOVA and paired t-tests were used to analyze time × group interactions. Conclusions: CBT offers an effective, scalable intervention for individuals with muscle dysmorphia complicated by anabolic steroid use. It promotes broad psychological improvement and may serve as a first-line treatment option in high-risk male fitness populations. Future studies should examine long-term outcomes and investigate implementation in diverse clinical and cultural contexts. Full article

A high intake of saturated fats and cholesterol from processed meats is associated with increased cardiovascular disease risk. This study aimed to develop a nutritionally enhanced Bologna-type sausage by partially replacing the beef content with a structured emulsion gel (EG) formulated from pine nut oil, inulin, carrageenan, and whey protein concentrate. The objective was to improve its lipid quality and functional performance while maintaining product integrity and consumer acceptability. Three sausage formulations were prepared: a control and two variants with 7% and 10% EG, which substituted for the beef content. The emulsion gel was characterized regarding its physical and thermal stability. Sausages were evaluated for their proximate composition, fatty acid profile, cholesterol content, pH, cooking yield, water-holding capacity, emulsion stability, instrumental texture, microstructure (via SEM), oxidative stability (TBARSs), and sensory attributes. Data were analyzed using a one-way and two-way ANOVA with Duncan’s test (p < 0.05). The EG’s inclusion significantly reduced the total and saturated fat and cholesterol, while increasing protein and unsaturated fatty acids. The 10% EG sample achieved a PUFA/SFA ratio of 1.00 and an over 80% reduction in atherogenic and thrombogenic indices. Functional improvements were observed in emulsion stability, cooking yield, and water retention. Textural and visual characteristics remained within acceptable sensory thresholds. SEM images showed more homogenous matrix structures in the EG samples. TBARS values increased slightly over 18 days of refrigeration but remained below rancidity thresholds. This period was considered a pilot-scale evaluation of oxidative trends. Sensory testing confirmed that product acceptability was not negatively affected. The partial substitution of beef content with pine nut oil-based emulsion gel offers a clean-label strategy to enhance the nutritional quality of Bologna-type sausages while preserving functional and sensory performance. This approach may support the development of health-conscious processed meat products aligned with consumer and regulatory demands. Full article

Mammography is the main method for early detection of breast cancer, which is still a major global health concern. However, inter-reader variability and the inherent difficulty of interpreting subtle radiographic features frequently limit the accuracy of diagnosis. A thorough assessment of deep convolutional neural networks (CNNs) for automated mammogram classification is presented in this work, along with the introduction of two innovative multi-view integration techniques: Dual-Branch Ensemble (DBE) and Merged Dual-View (MDV). By setting aside two datasets for out-of-sample testing, we evaluate the generalizability of the model using six different mammography datasets that represent various populations and imaging systems. We compare a number of cutting-edge architectures on both individual and combined datasets, including ResNet, DenseNet, EfficientNet, MobileNet, Vision Transformers, and VGG19. Both MDV and DBE strategies improve classification performance, according to experimental results. VGG19 and DenseNet both obtained high ROC AUC scores of 0.9051 and 0.7960 under the MDV approach. DenseNet demonstrated strong performance in the DBE setting, achieving a ROC AUC of 0.8033, while ResNet50 recorded a ROC AUC of 0.8042. These enhancements demonstrate how beneficial multi-view fusion is for boosting model robustness. The impact of domain shift is further highlighted by generalization tests, which emphasize the need for diverse datasets in training. These results offer practical advice for improving CNN architectures and integration tactics, which will aid in the creation of trustworthy, broadly applicable AI-assisted breast cancer screening tools. Full article

Fagonia cretica, a medicinal herb from the Zygophyllaceae family, is traditionally utilized to treat various conditions such as hepatitis, gynecological disorders, tumors, urinary tract issues, and diabetes. The present study aimed to evaluate the therapeutic potential of Fagonia cretica in treating polycystic ovarian syndrome (PCOS) induced in female rats. PCOS, a complex hormonal disorder, was experimentally induced by administering Letrozole (1 mg/kg) in combination with a high-fat diet for 21 days. The affected rats were then treated with hydro-alcoholic extracts of Fagonia cretica at doses of 100 mg/kg, 200 mg/kg, and 300 mg/kg for 20 days. Key biochemical parameters—including serum testosterone, insulin, fasting blood glucose, insulin resistance (HOMA-IR), cholesterol, triglycerides, and lipoprotein levels—were measured. Ultrasound imaging and histopathological analysis of ovarian tissues were also performed. The data were analyzed using computer-based statistical tools, including one-way ANOVA, Cohen’s d effect size, and Tukey’s HSD test, with graphical representations generated using Python 3.10 on the Kaggle platform. Results demonstrated a significant reduction in serum testosterone, insulin, cholesterol, and triglyceride levels (p < 0.05) in treated groups, along with improved ovarian morphology. These findings support the therapeutic potential of Fagonia cretica as a natural treatment for PCOS. Full article