Search Results (4,437)

Background: Gliomas, including the most severe form known as glioblastomas, are primary brain tumors arising from glial cells, with significant impact on adults, particularly men aged 45 to 70. Recent advancements in the WHO (World Health Organization) classification now correlate genetic markers with glioma phenotypes, enhancing diagnostic precision and therapeutic strategies. Aims and Methods: This scoping review aims to evaluate the current state of deep learning (DL) applications in the genetic characterization of adult gliomas, addressing the potential of these technologies for a reliable virtual biopsy. Results: We reviewed 17 studies, analyzing the evolution of DL algorithms from fully convolutional networks to more advanced architectures (ResNet and DenseNet). The methods involved various validation techniques, including k-fold cross-validation and external dataset validation. Conclusions: Our findings highlight significant variability in reported performance, largely due to small, homogeneous datasets and inconsistent validation methods. Despite promising results, particularly in predicting individual genetic traits, the lack of robust external validation limits the generalizability of these models. Future efforts should focus on developing larger, more diverse datasets and integrating multidisciplinary collaboration to enhance model reliability. This review underscores the potential of DL in advancing glioma characterization, paving the way for more precise, non-invasive diagnostic tools. The development of a robust algorithm capable of predicting the somatic genetics of gliomas or glioblastomas could accelerate the diagnostic process and inform therapeutic decisions more quickly, while maintaining the same level of accuracy as the traditional diagnostic pathway, which involves invasive tumor biopsies. Full article

Background/Objective: This study aims to utilize advanced artificial intelligence (AI) image recog-nition technologies to establish a robust system for identifying features in lung computed tomog-raphy (CT) scans, thereby detecting respiratory infections such as SARS-CoV-2 pneumonia. Spe-cifically, the research focuses on developing a new model called Residual-Dense-Attention Gates U-Net (RDAG U-Net) to improve accuracy and efficiency in identification. Methods: This study employed Attention U-Net, Attention Res U-Net, and the newly developed RDAG U-Net model. RDAG U-Net extends the U-Net architecture by incorporating ResBlock and DenseBlock modules in the encoder to retain training parameters and reduce computation time. The training dataset in-cludes 3,520 CT scans from an open database, augmented to 10,560 samples through data en-hancement techniques. The research also focused on optimizing convolutional architectures, image preprocessing, interpolation methods, data management, and extensive fine-tuning of training parameters and neural network modules. Result: The RDAG U-Net model achieved an outstanding accuracy of 93.29% in identifying pulmonary lesions, with a 45% reduction in computation time compared to other models. The study demonstrated that RDAG U-Net performed stably during training and exhibited good generalization capability by evaluating loss values, model-predicted lesion annotations, and validation-epoch curves. Furthermore, using ITK-Snap to convert 2D pre-dictions into 3D lung and lesion segmentation models, the results delineated lesion contours, en-hancing interpretability. Conclusion: The RDAG U-Net model showed significant improvements in accuracy and efficiency in the analysis of CT images for SARS-CoV-2 pneumonia, achieving a 93.29% recognition accuracy and reducing computation time by 45% compared to other models. These results indicate the potential of the RDAG U-Net model in clinical applications, as it can accelerate the detection of pulmonary lesions and effectively enhance diagnostic accuracy. Additionally, the 2D and 3D visualization results allow physicians to understand lesions' morphology and distribution better, strengthening decision support capabilities and providing valuable medical diagnosis and treatment planning tools. Full article

Technology has the potential to address the unique needs of young stroke survivors. Despite this, little is known about the technological resources available to support young adults with stroke. This study aimed to identify and compile available technological resources that cater to the specific needs of young adults (18–30 years) with stroke in Australia. An environmental scan was conducted from December 2023 to March 2024. Sources included websites, app stores, rehabilitation centres, hospitals, organisations, technology developers, and healthcare professionals. Of the 114 resources identified, 11% were for re-training limb movement, 40% for speech rehabilitation, 20% for medication reminders, and 29% were social media posts offering peer mentoring and support. Most limb movement (75%) and medication reminder (87%) apps were free. However, most speech therapy apps (78%) had associated costs. Social media posts were hosted on Facebook (64%), Instagram (21%), TikTok (9%), YouTube (3%), and other websites (3%). Forty-six percent of the social media posts targeting young stroke survivors did not specify the age group. These resources were identified as available to young people with stroke. Although the resources found focused on young stroke survivors, it was difficult to ascertain the specific age group that was being targeted. Full article

This project aims to develop a recommendation system to mitigate looping issues in HDD slider testing using the Amber testing machine (Machine A). Components simulating the HDD often fail and require repair before re-testing. However, post-repair, there is a 34% probability that the component (referred to as Product A) will experience looping, characterized by repeated failures with error code A. This recurring issue significantly hampers testing efficiency by reducing the number of successful slider tests. To address this challenge, we propose a dual-approach recommendation system that provides technicians with actionable insights to minimize the occurrence of looping. For previously analyzed components, a collaborative filtering technique utilizing implicit ratings is employed to generate recommendations. For new components, for which prior data are unavailable, a cosine similarity approach is applied to suggest optimal actions. An automatic training system is implemented to retrain the model as new data become available, ensuring that the recommendation system remains robust and effective over time. The proposed system is expected to offer precise guidance to technicians, thereby improving the overall efficiency of the testing process by reducing the frequency of looping issues. This work represents a significant advancement in enhancing operational reliability and productivity in HDD slider testing. Full article

In the field of Generalized Zero-Shot Learning (GZSL), the challenge lies in learning attribute-based information from seen classes and effectively conveying this knowledge to recognize both seen and unseen categories during the training process. This paper proposes an innovative approach to enhance the generalization ability and efficiency of GZSL models by integrating a Convolutional Block Attention Module (CBAM). The CBAM blends channel-wise and spatial-wise information to emphasize key features, thereby improving the model’s discriminative and localization capabilities. Additionally, the method employs a ResNet101 backbone for systematic image feature extraction, enhanced contrastive learning, and a similarity map generator with attribute prototypes. This comprehensive framework aims to achieve robust visual–semantic embedding for classification tasks. The proposed method demonstrates significant improvements in performance metrics in benchmark datasets, showcasing its potential in advancing GZSL applications. Full article

Background: Aortic coarctation (CoA) is a congenital heart disease affecting 5–8% of patients, with long-term complications persisting despite successful correction. Stress echocardiography (SE) is increasingly used for evaluating cardiac function under stress, yet its role in repaired CoA remains under-explored. Objective: This study aimed to assess the predictive value of SE and myocardial strain in repaired CoA patients with a history of hypertension without significant gradients or with borderline gradients at rest. Methods: Between June 2020 and March 2024, we enrolled 35 consecutive CoA patients with successful repairs and either a history of hypertension or borderline Doppler gradients. Baseline and peak exercise echocardiographic measurements, including left ventricular mass index (LVMi) and global longitudinal strain (LVGLS), were recorded. Patients were followed for up to 4 years. Results: At baseline, the positive SE group had higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) compared to the negative SE group. The positive SE group also exhibited significantly higher basal and peak trans-isthmic gradients. Positive SE was found in 45.7% of patients, with 68.7% of these requiring re-intervention during follow-up. A peak trans-isthmic gradient > 61 mmHg during exercise predicted recoarctation with 100% sensitivity and 71% specificity (AUC = 0.836, p < 0.004). Conclusions: SE identifies at-risk patients post-CoA repair, aiding in early intervention. A peak trans-isthmic gradient > 61 mmHg during exercise is a strong predictor of recoarctation. These findings support incorporating SE into routine follow-up protocols for CoA patients, particularly those with a history of hypertension and borderline gradients, to improve long-term outcomes and quality of life. Full article

Background/Objectives: Laryngeal squamous cell carcinoma (LSCC) is among most frequent malignancies of the head and neck. Recent oncologic research focusses on advanced rather than on early stages. Thus, we aimed to improve the knowledge concerning prognostic factors and survival in early glottic (GC) and supraglottic cancer (SGC). Methods: We retrospectively investigated patients diagnosed in 2007 to 2020 with stage I or II GC (ICD-10-C32.0) or SGC (ICD-10-C32.1, C32.8 or C32.9). For precise discrimination of GC and SGC, pathology reports about biopsy and definitive excision were closely examined and information on clinical characteristics and risk factors were collected before analyzing patterns of risk factors for overall survival (OS) in multivariate Cox regression analyses (mvCox). Results: The cohort included 220 patients with early GC (n = 183) and SGC (n = 37). The GC patients showed significantly improved 5-year OS compared to SGC patients (83.6% vs. 64.9%; p = 0.004), whereas survival according to UICC stage (I vs. II) was not different (p = 0.177). Surgical resection was superior to definitive radiotherapy (RT) for 5-year OS (p < 0.001). Cumulative tobacco consumption of greater than 10 pack years drastically impaired OS (p = 0.024), especially in patients receiving RT (p < 0.001). Supraglottic localization, smoking, and re-resection after initial R1 status consistently were independent prognostic factors in mvCox. Conclusions: Our cohort of early LSCC patients demonstrates significant negative impact of supraglottic localization, older age, tobacco consumption, poor tumor differentiation, and re-resection on OS. Further research is required as there is still lack of evidence on optimal decision-making and therapeutic strategies. Full article

Aiming at the problem of accurate AC series arc fault detection, this paper proposes a low voltage AC series arc fault intelligent detection model based on deep learning. According to the GB/T 31143—2014 standard, an experimental platform was established. This system comprises a lower computer (slave computer) and an upper computer (master computer). It facilitates the acquisition of experimental data and the detection of arc faults during the data acquisition process. Based on a one-dimensional Convolutional Neural Network (CNN), Residual model (Res) and RIME optimization algorithm (RIME) are introduced to optimize the CNN. The current signals collected using high-frequency current, low-frequency coupled current, and high-frequency coupled current are used to construct an arc fault feature set for training of the necessary detection model. The experimental results indicate that the RIME optimization algorithm delivers the best performance when optimizing a one-dimensional CNN detection model with an introduced Res. This model achieves a detection accuracy of 99.42% ± 0.13% and a kappa coefficient of 95.69% ± 0.96%. For collection methods, high-frequency coupled current signals are identified as the optimal choice for detecting low-voltage AC series arc faults. Regarding feature selection, random forest-based feature importance ranking proves to be the most effective method. Full article

In addition to common skin pathogens, acute focal lymphadenitis in humans can, in rare cases, be caused by a zoonotic pathogen. Furthermore, it can develop in the absence of any direct or indirect contact with infected animals, in cases when the microorganism is transmitted by a vector. These clinical entities are rare, and therefore often not easily recognized, yet many zoonotic illnesses are currently considered emerging or re-emerging in many regions. Focal zoonotic vector-borne lymphadenitis and its numerous causative agents, with their variegated clinical manifestations, have been described in some case reports and small case series. Therefore, we summarized those data in this narrative overview, with the aim of raising clinical awareness, which could improve clinical outcomes. This overview briefly covers reported pathogens, their vectors and geographic distribution, and their main clinical manifestations, diagnostic possibilities, and recommended therapy. Vector-borne tularemia, plague, bartonellosis, rickettsioses, borreliosis, and Malayan filariasis are mentioned. According to the existing data, when acute focal bacterial vector-borne zoonotic lymphadenitis is suspected, in severe or complicated cases it seems prudent to apply combined aminoglycoside (or quinolone) plus doxycycline as an empirical therapy, pending definite diagnostic results. In this field, the “one health approach” and further epidemiological and clinical studies are needed. Full article

This paper presents a comprehensive study of five machine learning (ML) algorithms for predicting key characteristics of Reinforced Concrete (RC) structural systems. A novel dataset, ModRes, consisting of 9723 examples derived from modal and response spectrum analyses on masonry-infilled three-dimensional RC buildings, was created for ML applications. The primary objective is to develop an ML model using five distinct algorithms from the literature, capable of concurrently predicting torsional irregularity, modal participating mass ratio (MPMR), and the fundamental period in a 3D environment, while accounting for the influence of infill walls. Additionally, the study aims to determine the applicability of pushover analysis (POA) without the need for extensive numerical modeling and analysis. This approach optimizes the preliminary design process with minimal computational effort, providing valuable insights into dynamic and torsional responses during seismic events. The Categorical Boosting algorithm demonstrated outstanding performance, achieving R² values of 0.977 for torsional irregularity, 0.997 for the fundamental period, and 0.923 for MPMR on the test dataset. It also successfully predicted POA applicability with an error rate of only 1.36%. This study highlights the practical application of ML algorithms, underscoring their effectiveness in structural engineering. Full article

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. This study aims to compare the performance of three deep learning object detection models—Faster R-CNN, YOLO V2, and SSD—using different ResNet architectures (ResNet-18, ResNet-50, and ResNet-101) as feature extractors for detecting and classifying third molar angles in panoramic X-rays according to Winter’s classification criterion. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using panoramic X-rays by using Winter’s classification criterion. This criterion characterizes the third molar’s position relative to the second molar’s longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-rays. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic radiographs represents a promising solution in dental applications. Full article

Background/Objectives: The accurate quantification of peripheral nerve axonal regeneration after injury is critically important. Current strategies are limited to detecting early reinnervation. DTI is an MRI modality permitting the assessment of fractional anisotropy, which increases with axonal regeneration. The aim of this pilot study is to evaluate DTI as a potential predictive factor of clinical outcome after median nerve section and microsurgical repair. Methods: We included 10 patients with a complete section of the median nerve, who underwent microsurgical repair up to 7 days after injury. The follow-up period was 1 year, including the current strategy with clinical visits, the Rosén–Lundborg score and electroneuromyography. Additionally, DTI MRI of the injured wrist was planned 1, 3 and 12 months post-operatively and once for the contralateral wrist. Results: The interobserver reliability of DTI measures was almost perfect (ICC 0.802). We report an early statistically significant increase in the fractional anisotropy value after median nerve repair, especially in the region located distal to the suture. Meanwhile, Rosén–Lundborg score gradually increased between the third and sixth month, and continued to increase between the sixth and twelfth month. Conclusions: DTI outcomes three months post-operation could offer greater predictability compared to current strategies. This would enable faster decision-making regarding the need for a potential re-operation in cases of inadequate early reinnervation. Full article

Chronic wounds complicated by infection pose significant clinical challenges, necessitating comprehensive treatment approaches. The widespread use of antibiotics has led to resistant microorganisms, complicating traditional therapies. This study aims to develop and evaluate modified alginate wound dressings with enhanced antimicrobial and regenerative properties. Alginate dressings were synthesized with silver nanoparticles, cefepime, and fibroblast growth factor-2 (FGF-2). The two-stage therapy involved an initial antibacterial dressing followed by a regenerative dressing. In vitro tests demonstrated high antibacterial activity, with maximum inhibition zones for P. aeruginosa (41.3 ± 0.4 mm) and S. aureus (36.6 ± 1.8 mm). In vivo studies on rats with purulent wounds showed significant healing progression in the experimental group. Histological analysis revealed complete re-epithelialization, thicker neoepithelium, dense collagen deposition, and minimal inflammation in treated wounds. These findings suggest that the modified alginate dressings significantly enhance the reparative process and are promising for treating chronic infected wounds in both veterinary and medical practices. Full article

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes. Full article

In the process of using multi-channel ground-penetrating radar (GPR) for underground cavity exploration, the acquired 3D data include reflection data from underground cavities or various underground objects (structures). Reflection data from unspecified structures can interfere with the identification process of underground cavities. This study aims to identify underground cavities using a C-GAN model with an applied ResBlock technique. This deep learning model demonstrates excellent performance in the image domain and can automatically classify the presence of cavities by analyzing 3D GPR data, including reflection waveforms (A-scan), cross-sectional views (B-scan), and plan views (C-scan) measured from the ground under roads. To maximize the performance of the C-GAN model, a background filtering algorithm (BFA) was developed and applied to enhance the visibility and clarity of underground cavities. To verify the performance of the developed BFA, 3D data collected from roads in Seoul, Republic of Korea, using 3D GPR equipment were transformed, and the C-GAN model was applied. As a result, it was confirmed that the recall, an indicator of cavity prediction, improved by approximately 1.15 times compared to when the BFA was not applied. This signifies the verification of the effectiveness of the BFA. This study developed a special algorithm to distinguish underground cavities. This means that in the future, not only the advancement of separate equipment and systems but also the development of specific algorithms can contribute to the cavity exploration process. Full article