Search Results (1,486)

In the original randomised Dietary Intervention to Stop Coronary Atherosclerosis (DISCO-CT) trial, a 12-month Dietary Approaches to Stop Hypertension (DASH) project led by dietitians improved cardiovascular and metabolic risk factors and reduced platelet chemokine levels in patients with coronary artery disease (CAD). It is unclear whether these benefits are sustained. Objective: To determine whether the metabolic, inflammatory, and clinical benefits achieved during the DISCO-CT trial are sustained six years after the structured intervention ended. Methods: Ninety-seven adults with non-obstructive CAD confirmed in coronary computed tomography angiography were randomly assigned to receive optimal medical therapy (control group, n = 41) or the same therapy combined with intensive DASH counselling (DASH group, n = 43). After 301 ± 22 weeks, 84 individuals (87%) who had given consent underwent reassessment of body composition, meal frequency assessment, and biochemical testing (lipids, hs-CRP, CXCL4, RANTES and homocysteine). Major adverse cardiovascular events (MACE) were assessed. Results: During the intervention, the DASH group lost an average of 3.6 ± 4.2 kg and reduced their total body fat by an average of 4.2 ± 4.8 kg, compared to an average loss of 1.1 ± 2.9 kg and a reduction in total body fat of 0.3 ± 4.1 kg in the control group (both p < 0.01). Six years later, most of the lost body weight and fat tissue had been regained, and there was a sharp increase in visceral fat area in both groups (p < 0.0001). CXCL4 decreased by 4.3 ± 3.0 ng/mL during the intervention and remained lower than baseline values; in contrast, in the control group, it initially increased and then decreased (p < 0.001 between groups). LDL cholesterol and hs-CRP levels returned to baseline in both groups but remained below baseline in the DASH group. There was one case of MACE in the DASH group, compared with four cases (including one fatal myocardial infarction) in the control group (p = 0.575). Overall adherence to the DASH project increased by 26 points during counselling and then decreased by only four points, remaining higher than in the control group. Conclusions: A one-year DASH project supported by a physician and dietitian resulted in long-term suppression of the proatherogenic chemokine CXCL4 and fewer MACE over six years, despite a decline in adherence and loss of most anthropometric and lipid benefits. It appears that sustained systemic reinforcement of behaviours is necessary to maintain the benefits of lifestyle intervention in CAD. Full article

Background/Objective: The COVID-19 pandemic profoundly transformed social life worldwide, indiscriminately affecting individuals across all age groups. Children have not been exempted from the risk of severe illness and death caused by COVID-19. Objective: This paper sought to describe the clinical findings, laboratory and imaging results, and hospital care provided for severe and critical cases of COVID-19 in unvaccinated children, with or without severe asthma, hospitalized in a public referral service for COVID-19 treatment in the Brazilian state of Pernambuco. Methods: This was a case series study of severe and critical COVID-19 in hospitalized, unvaccinated children, with or without severe asthma, conducted in a public referral hospital between March 2020 and June 2021. Results: The case series included 80 children, aged from 1 month to 11 years, with the highest frequency among those under 2 years old (58.8%) and a predominance of males (65%). Respiratory diseases, including severe asthma, were present in 73.8% of the cases. Pediatric multisystem inflammatory syndrome occurred in 15% of the children, some of whom presented with cardiac involvement. Oxygen therapy was required in 65% of the cases, mechanical ventilation in 15%, and 33.7% of the children required intensive care in a pediatric intensive care unit. Pulmonary infiltrates and ground-glass opacities were common findings on chest X-rays and CT scans; inflammatory markers were elevated, and the most commonly used medications were antibiotics, bronchodilators, and corticosteroids. Conclusions: This case series has identified key characteristics of children with severe and critical COVID-19 during a period when vaccines were not yet available in Brazil for the study age group. However, the persistence of low vaccination coverage, largely due to parental vaccine hesitancy, continues to leave children vulnerable to potentially severe illness from COVID-19. These findings may inform the development of public health emergency contingency plans, as well as clinical protocols and care pathways, which can guide decision-making in pediatric care and ensure appropriate clinical management, ultimately improving the quality of care provided. Full article

A stroke is a critical medical condition and one of the leading causes of death among humans. Segmentation of the lesions of the brain in which the blood flow is impeded because of blood coagulation plays a vital role in drug prescription and medical diagnosis. Computed tomography (CT) scans play a crucial role in detecting abnormal tissue. There are several methods for segmenting medical images that utilize the main images without considering the patient’s privacy information. In this paper, a deep network is proposed that utilizes compressive sensing and ensemble learning to protect patient privacy and segment the dataset efficiently. The compressed version of the input CT images from the ISLES challenge 2018 dataset is applied to the ensemble part of the proposed network, which consists of two multi-resolution modified U-shaped networks. The evaluation metrics of accuracy, specificity, and dice coefficient are 92.43%, 91.3%, and 91.83%, respectively. The comparison to the state-of-the-art methods confirms the efficiency of the proposed compressive sensing-based ensemble net (CS-Ensemble Net). The compressive sensing part provides information privacy, and the parallel ensemble learning produces better results. Full article

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Background/Objectives: Rib fracture detection holds critical importance in the field of medical image processing. Methods: In this study, two different data augmentation methods, traditional data augmentation (Albumentations) and focused data augmentation (focused augmentation), were compared using computed tomography (CT) images for the detection of rib fractures on YOLOv8n, YOLOv8s, and YOLOv8m models. While the traditional data augmentation method applies general transformations to the entire image, the focused data augmentation method performs specific transformations by targeting only the fracture regions. Results: The model performance was evaluated using the Precision, Recall, mAP@50, and mAP@50–95 metrics. The findings revealed that the focused data augmentation method achieved superior performance in certain metrics. Specifically, analysis on the YOLOv8s model showed that the focused data augmentation method increased the mAP@50 value by 2.18%, reaching 0.9412, and improved the recall value for fracture detection by 5.70%, reaching 0.8766. On the other hand, the traditional data augmentation method achieved better results in overall precision metrics with the YOLOv8m model and provided a slight advantage in the mAP@50 value. Conclusions: The study indicates that focused data augmentation can contribute to achieving more reliable and accurate results in medical imaging applications. Full article

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

This study evaluated the effects of low-dose recombinant human bone morphogenetic protein-2 (rhBMP-2) coating in combination with vacuum plasma treatment on titanium implants, aiming to enhance osseointegration and bone regeneration while minimizing the adverse effects associated with high-dose rhBMP-2. In vitro analyses demonstrated that plasma treatment increased surface energy, promoting cell adhesion and proliferation. Additionally, it facilitated sustained rhBMP-2 release by enhancing protein binding to the implant surface. In vivo experiments using the four-beagle mandibular defect model were conducted with the following four groups: un-treated implants, rhBMP-2–coated implants, plasma-treated implants, and implants treated with both rhBMP-2 and plasma. Micro-computed tomography (micro-CT) and medical CT analyses revealed a significantly greater volume of newly formed bone in the combined treatment group (p < 0.05). Histological evaluation further confirmed superior outcomes in the combined group, showing significantly higher bone-to-implant contact (BIC), new bone area (NBA), and inter-thread bone density (ITBD) compared to the other groups (p < 0.05). These findings indicate that vacuum plasma treatment enhances the biological efficacy of low-dose rhBMP-2, representing a promising strategy to improve implant integration in compromised conditions. Further studies are warranted to determine the optimal clinical dosage. Full article

Malignant mediastinal tumors are a group representing some of the most demanding oncological challenges for early, multi-level, and successful management. The timely identification of any suspicious clinical symptomatology is urgent in achieving an accurate, staged histological diagnosis, in order to follow up with an equally detailed medical therapeutic plan (interventional or not) and determine the principal goals regarding efficient overall treatment in these patients. We report a case of a 24-year-old male patient with an incident-free prior medical history. An initial chest X-ray was performed after the patient reported short-term, consistent moderate chest pain symptomatology, early work fatigue, and shortness of breath. The following imaging procedures (chest CT, PET-CT) indicated the presence of an anterior mediastinal mass (meas. ~11 cm × 10 cm × 13 cm, SUV: 8.7), applying additional pressure upon both right heart chambers. The Alpha-Fetoprotein (aFP) blood levels had exceeded at least 50 times their normal range. Two consecutive diagnostic attempts with non-specific histological results, a negative-for-malignancy fine-needle aspiration biopsy (FNA-biopsy), and an additional tumor biopsy, performed via mini anterior (R) thoracotomy with “suspicious” cellular gatherings, were performed elsewhere. After admission to our department, an (R) Video-Assisted Thoracic Surgery (VATS) was performed, along with multiple tumor biopsies and moderate pleural effusion drainage. The tumor’s measurements had increased to DMax: 16 cm × 9 cm × 13 cm, with a severe degree of atelectasis of the Right Lower Lobe parenchyma (RLL) and a pressure-displacement effect upon the Superior Vena Cava (SVC) and the (R) heart sinus, based on data from the preoperative chest MRA. The histological report indicated elements of a combined, non-seminomatous germ-cell mediastinal tumor, posthuberal-type teratoma, and embryonal carcinoma. The imminent chemotherapeutic plan included a “BEP” (Bleomycin^®/Cisplatin^®/Etoposide^®) scheme, which needed to be modified to a “VIP” (Cisplatin^®/Etoposide^®/Ifosfamide^®) scheme, due to an acute pulmonary embolism incident. While the aFP blood levels declined, even reaching normal measurements, the tumor’s size continued to increase significantly (DMax: 28 cm × 25 cm × 13 cm), with severe localized pressure effects, rapid weight loss, and a progressively worsening clinical status. Thus, an emergency surgical intervention took place via median sternotomy, extended with a complementary “T-Shaped” mini anterior (R) thoracotomy. A large, approx. 4 Kg mediastinal tumor was extracted, with additional RML and RUL “en-bloc” segmentectomy and partial mediastinal pleura decortication. The following histological results, apart from verifying the already-known posthuberal-type teratoma, indicated additional scattered small lesions of combined high-grade rabdomyosarcoma, chondrosarcoma, and osteosarcoma, as well as numerous high-grade glioblastoma cellular gatherings. No visible findings of the previously discovered non-seminomatous germ-cell and embryonal carcinoma elements were found. The patient’s postoperative status progressively improved, allowing therapeutic management to continue with six “TIP” (Cisplatin^®/Paclitaxel^®/Ifosfamide^®) sessions, currently under his regular “follow-up” from the oncological team. This report underlines the importance of early, accurate histological identification, combined with any necessary surgical intervention, diagnostic or therapeutic, as well as the appliance of any subsequent multimodality management plan. The diversity of mediastinal tumors, especially for young patients, leaves no place for complacency. Such rare examples may manifest, with equivalent, unpredictable evolution, obliging clinical physicians to stay constantly alert and not take anything for granted. Full article

Lung nodule detection and segmentation are essential tasks in computer-aided diagnosis (CAD) systems for early lung cancer screening. With the growing availability of CT data and deep learning models, researchers have explored various strategies to improve the performance of these tasks. This review focuses on Multi-Task Learning (MTL) approaches, which unify or cooperatively integrate detection and segmentation by leveraging shared representations. We first provide an overview of traditional and deep learning methods for each task individually, then examine how MTL has been adapted for medical image analysis, with a particular focus on lung CT studies. Key aspects such as network architectures and evaluation metrics are also discussed. The review highlights recent trends, identifies current challenges, and outlines promising directions toward more accurate, efficient, and clinically applicable CAD solutions. The review demonstrates that MTL frameworks significantly enhance efficiency and accuracy in lung nodule analysis by leveraging shared representations, while also identifying critical challenges such as task imbalance and computational demands that warrant further research for clinical adoption. Full article

The concept of “more is better” has long dominated cancer treatment, emphasizing aggressive therapies despite their toxicity. However, the rise of personalized medicine has fostered treatment de-escalation strategies aimed at minimizing toxicity, improving quality of life, and reducing costs. This position paper highlights key applications of de-escalation in medical oncology, with a primary focus on breast cancer and notable examples in colorectal, head and neck, ovarian, lung, and prostate cancers. Various approaches, including dose reduction, treatment duration shortening, and regimen optimization, have demonstrated efficacy without compromising clinical outcomes. Advances in molecular diagnostics, such as Oncotype Dx in breast cancer and circulating tumor DNA (ctDNA) analysis in colorectal cancer, have facilitated patient selection for de-escalation. While these strategies present promising results, challenges remain, particularly in balancing treatment intensity with oncologic control. The review underscores the need for further prospective trials to refine de-escalation approaches and ensure their safe integration into standard oncologic care. Full article

Using neural networks has become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformer architectures, including Swin Transformer and MaxViT, for several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset included different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims to analyze the behavior of the neural networks in each dataset and the benefits of combining different image modalities and tumor classes. We designed several experiments by fine-tuning the models on combined and individual datasets. The results revealed that the Swin Transformer achieved the highest accuracy, with an average of 99.0% on single datasets and reaching 99.43% on the combined dataset. This research highlights the adaptability of Transformer-based models to various human organs and image modalities. The main contribution lies in evaluating multiple ViT architectures across multi-organ tumor datasets, demonstrating their generalization to multi-organ classification. Integrating these models across diverse datasets could mark a significant advance in precision medicine, paving the way for more efficient healthcare solutions. Full article

Background: Hypometabolism of the thalamus has been reported in epilepsy patients. This study aimed to investigate the prognostic value of thalamic metabolic activity in children with epilepsy. Methods: A total of 200 children with epilepsy and 237 children without epilepsy (sex- and age-matched control group) underwent 18F-FDG PET/CT in this study. Localization of the interictal hypometabolic epileptic focus was performed visually. Bilateral thalamic metabolic activity was evaluated qualitatively (thalamic FDG uptake in relation to the cerebral cortex) and semi-quantitatively (SUV max, normalized SUV (ratio to ipsilateral cerebellum), and absolute asymmetric index (AAI). Results: A total of 133 patients (66.5%) with epilepsy showed cerebral cortical hypometabolism in the interictal 18F-FDG PET study; there were 76 patients on the right side, 55 patients on the left side, and two patients on both sides. Of these 133 patients, 45 also had visually observed asymmetric hypometabolism in the thalamus. Semi-quantitatively, asymmetry was more prominent in epileptic patients. AAI was a more sensitive variable than other variables. Average AAIs were 3.89% and 7.36% in the control and epilepsy patients, respectively. Metabolic activity in the thalami was significantly reduced in epileptic patients compared to the control group. Associated hypometabolism of the ipsilateral thalamus was observed in 66.5% of epileptic patients with a focal cortical defect semi-quantitatively. Overall, 61 out of 200 patients showed thalamus hypometabolism. Some 51 out of 61 patients (83.6%) with thalamus hypometabolism showed refractory disease; however, the refractory disease was noted in 90 out of 139 (64.7%) patients without thalamus hypometabolism. Brain surgery was performed in 86 epileptic patients (43%). Some 35 out of 86 patients had thalamus hypometabolism. Recurrence of epilepsy was observed more in patients with thalamus hypometabolism (48% vs. 25%), with p ≤ 0.01. Conclusion: This study suggests that patients with thalamus metabolic abnormalities may be more medically resistant to therapy and less responsive to surgical treatments. Therefore, the thalamus metabolic abnormality could be used as a prognostic sign in pediatric epilepsy. Recent studies have also suggested that incorporating thalamic metabolic data into clinical workflows may improve the stratification of treatment-resistant epilepsy in children. Full article

Machine learning algorithms are commonly employed for classification and interpretation of high-dimensional data. The classification task is often broken down into two separate procedures, and different methods are applied to achieve accurate results and produce interpretable outcomes. First, an effective subset of high-dimensional features must be extracted and then the selected subset will be used to train a classifier. Gradient Boosted Trees (GBT) is an ensemble model and, particularly due to their robustness, ability to model complex nonlinear interactions, and feature interpretability, they are well suited for complex applications. XGBoost (eXtreme Gradient Boosting) is a high-performance implementation of GBT that incorporates regularization, parallel computation, and efficient tree pruning that makes it a suitable efficient, interpretable, and scalable classifier with potential applications to medical data analysis. In this study, a Monte Carlo Gradient Boosted Trees (MCGBT) model is proposed for both feature reduction and classification. The proposed MCGBT method was applied to a lung cancer dataset for feature identification and classification. The dataset contains 107 radiomics which are quantitative imaging biomarkers extracted from CT scans. A reduced set of 12 radiomics were identified, and patients were classified into different cancer stages. Cancer staging accuracy of 90.3% across 100 independent runs was achieved which was on par with that obtained using the full set of 107 radiomics, enabling lean and deployable classifiers. Full article

Over recent decades, significant advancements have been made in the treatment and imaging of gliomas. Conventional imaging techniques, such as MRI and CT, play critical roles in glioma diagnosis and treatment but often fail to distinguish between tumor pseudoprogression (Psp) and radiation necrosis (RN) versus true progression (TP). Emerging fields like radiomics and radiogenomics are addressing these challenges by extracting quantitative features from medical images and correlating them with genomic data, respectively. This article will discuss several studies that show how radiomic features (RFs) can aid in better patient stratification and prognosis. Radiogenomics, particularly in predicting biomarkers such as MGMT promoter methylation and 1p/19q codeletion, shows potential in non-invasive diagnostics. Radiomics also offers tools for predicting tumor recurrence (rBT), essential for treatment management. Further research is needed to standardize these methods and integrate them into clinical practice. This review underscores radiomics and radiogenomics’ potential to revolutionize glioma management, marking a significant shift towards precision neuro-oncology. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by integrating smart diagnostic devices with cloud computing and real-time data analytics. The emergence of infectious diseases, including COVID-19, underscores the need for rapid and decentralized diagnostics to facilitate early intervention. Traditional centralized laboratory testing introduces delays, limiting timely medical responses. While point-of-care molecular diagnostic (POC-MD) systems offer an alternative, challenges remain in cost, accessibility, and network inefficiencies. This study proposes an IoMT-based architecture for fully automated POC-MD devices, leveraging WebSockets for optimized communication, enhancing microfluidic cartridge efficiency, and integrating a hardware-based emulator for real-time validation. The system incorporates DNA extraction and real-time polymerase chain reaction functionalities into modular, networked components, improving flexibility and scalability. Although the system itself has not yet undergone clinical validation, it builds upon the core cartridge and detection architecture of a previously validated cartridge-based platform for Chlamydia trachomatis and Neisseria gonorrhoeae (CT/NG). These pathogens were selected due to their global prevalence, high asymptomatic transmission rates, and clinical importance in reproductive health. In a previous clinical study involving 510 patient specimens, the system demonstrated high concordance with a commercial assay with limits of detection below 10 copies/μL, supporting the feasibility of this architecture for point-of-care molecular diagnostics. By addressing existing limitations, this system establishes a new standard for next-generation diagnostics, ensuring rapid, reliable, and accessible disease detection. Full article