Search Results (844)

Inflammatory Bowel Diseases (IBDs) are complex, multifactorial disorders with no known cure, necessitating lifelong care and often leading to surgical interventions. This ongoing healthcare requirement, coupled with the increased use of biological drugs and rising disease prevalence, significantly increases the financial burden on the healthcare systems. Thus, a number of novel technological approaches have emerged in order to face some of the pivotal questions still associated with IBD. In navigating the intricate landscape of IBD, biosensors act as indispensable allies, bridging the gap between traditional diagnostic methods and the evolving demands of precision medicine. Continuous progress in biosensor technology holds the key to transformative breakthroughs in IBD management, offering more effective and patient-centric healthcare solutions considering the One Health Approach. Here, we will delve into the landscape of biomarkers utilized in the diagnosis, monitoring, and management of IBD. From well-established serological and fecal markers to emerging genetic and epigenetic markers, we will explore the role of these biomarkers in aiding clinical decision-making and predicting treatment response. Additionally, we will discuss the potential of novel biomarkers currently under investigation to further refine disease stratification and personalized therapeutic approaches in IBD. By elucidating the utility of biosensors across the spectrum of IBD care, we aim to highlight their importance as valuable tools in optimizing patient outcomes and reducing healthcare costs. Full article

Background/Objectives: Accurate diagnosis of thyroid cancer is critical but challenging due to overlapping ultrasound (US) features of benign and malignant nodules. This study aimed to evaluate the diagnostic performance of non-invasive and minimally invasive US techniques, including B-mode US, shear wave elastography (SWE), color Doppler, superb microvascular imaging (SMI), and TI-RADS, in patients with suspected thyroid lesions and to assess their reliability and cost effectiveness compared with fine needle aspiration (FNA) biopsy. Methods: A prospective, single-center study (October 2023–February 2025) enrolled 300 patients with suspected thyroid cancer at a Spanish tertiary hospital. Of these, 296 patients with confirmed diagnoses underwent B-mode US, SWE, Doppler, SMI, and TI-RADS scoring, followed by US-guided FNA and Bethesda System cytopathology. Lasso-penalized logistic regression and a bootstrap analysis (1000 replicates) were used to develop diagnostic models. A utility function was used to balance diagnostic reliability and cost. Results: Thyroid cancer was diagnosed in 25 patients (8.3%). Elastography combined with SMI achieved the highest diagnostic performance (Youden index: 0.69; NPV: 97.4%; PPV: 69.1%), outperforming Doppler-only models. Intranodular vascularization was a significant risk factor, while peripheral vascularization was protective. The utility function showed that, when prioritizing cost, elastography plus SMI was cost effective (α < 0.716) compared with FNA. Conclusions: Elastography plus SMI offers a reliable, cost-effective diagnostic rule for thyroid cancer. The utility function aids clinicians in balancing reliability and cost. SMI and generalizability need to be validated in higher prevalence settings. Full article

Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image enhancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. We developed a customized image degradation pipeline simulating common artifacts in dermatological images, including blur, noise, downsampling, and compression. This synthetic degradation approach enabled effective training of DermaSR-GAN, a super-resolution generative adversarial network tailored for dermoscopic images. The model was trained on 30,000 high-quality ISIC images and evaluated on three independent datasets (ISIC Test, Novara Dermoscopic, PH²) using structural similarity and no-reference quality metrics. DermaSR-GAN achieved statistically significant improvements in quality scores across all datasets, with up to 23% enhancement in perceptual quality metrics (MANIQA). The model preserved diagnostic details while doubling resolution and surpassed existing approaches, including traditional interpolation methods and state-of-the-art deep learning techniques. Integration with downstream classification systems demonstrated up to 14.6% improvement in class-specific accuracy for keratosis-like lesions compared to original images. Synthetic degradation represents a promising approach for training effective super-resolution models in medical imaging, with significant potential for enhancing teledermatology applications and computer-aided diagnosis systems. Full article

Growing interest in the future applications of nanotechnology in medicine has led to groundbreaking developments in nanosensors. Nanosensors are excellent platforms that provide reliable solutions for continuous monitoring and real-time detection of clinical targets. Nanosensors have attracted great attention due to their remarkable sensitivity, portability, selectivity, and automated data acquisition. The exceptional nanoscale properties of nanomaterials used in the nanosensors boost their sensing potential even at minimal concentrations of analytes present in a clinical sample. Along with applications in diverse sectors, the beneficial aspects of nanosensors have been exploited in healthcare systems to utilize their applications in diagnosing, treating, and preventing diseases. Hence, in this review, we have presented an overview of the disease-prognostic applications of nanosensors in chronic diseases through a detailed literature analysis. We focused on the advances in various nanosensors in the field of major diseases such as cancer, cardiovascular diseases, diabetes mellitus, and neurodegenerative diseases along with other prevalent diseases. This review demonstrates various categories of nanosensors with different nanoparticle compositions and detection methods suitable for specific diagnostic applications in clinical settings. The chemical properties of different nanoparticles provide unique characteristics to each nanosensors for their specific applications. This will aid the detection of potential biomarkers or pathological conditions that correlate with the early detection of various diseases. The potential challenges and possible recommendations of the applications of nanosensors for disease diagnosis are also discussed. The consolidated information present in the review will help to better understand the disease-prognostic potentials of nanosensors, which can be utilized to explore new avenues in improved therapeutic interventions and treatment modalities. Full article

We planned a systemic review and meta-analysis to evaluate the diagnostic accuracy of Monocyte Distribution Width (MDW) in aiding the diagnosis of sepsis in the Emergency Department (ED) and Intensive Care Unit (ICU). A systematic literature search was performed in PubMed, Scopus, and OVID to retrieve studies published up to 29 January 2024. We examined results using mean difference and conducted a diagnostic test accuracy (DTA) meta-analysis using a bivariate random effects model. Pooled results showed that MDW was significantly higher in sepsis patients admitted to the ED (MD = 5.59, 95%CI: 4.14–7.05) or to the ICU (MD = 8.30, 95%CI: 2.98–13.62). Nine studies conducted in the ED were included in the DTA review. The overall sensitivity was 0.80 (95%CI: 0.75–0.85), the specificity was 0.76 (95%CI: 0.66–0.83), and the false-positive rate (FPR) was 0.24 (95%CI: 0.17–0.34). Three studies were conducted in the ICU, but only two were included in the DTA meta-analysis. Of the 662 patients admitted to the ICU, 175 developed sepsis, showing higher MDW values than non-septic patients. However, significant heterogeneity was noted among the studies. MDW is a helpful biomarker for sepsis in adult patients admitted to the ED and ICU. In the ED, MDW could aid clinicians in ruling out sepsis. Full article

Rubinstein–Taybi Syndrome type 1 (RSTS1) is an uncommon autosomal dominant genetic disorder associated with neurodevelopmental impairments and multiple congenital anomalies, with an incidence of 1:100,000–125,000 live births. The syndrome, caused by de novo mutations in the CREBBP gene, is characterized by phenotypic variability, including intellectual disability, facial dysmorphisms, and systemic abnormalities. The current case report describes a 15-year-old Brazilian female diagnosed with RSTS1 through whole-exome sequencing, which identified a de novo heterozygous missense mutation in the CREBBP gene (NM_004380.3; c.4393G > C; p.Gly1465Arg), classified as pathogenic. The patient’s clinical presentation included facial dysmorphisms, skeletal abnormalities, neurodevelopmental delay, psychiatric conditions, and other systemic manifestations. A comprehensive genetic counseling process facilitated the differential diagnosis and management strategies, emphasizing the importance of early and precise diagnosis for improving clinical outcomes. This report contributes to the growing knowledge of the genotype–phenotype correlations in RSTS1, aiding in the understanding and management of this uncommon condition. 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

Swine infectious diseases, often caused by multiple co-infecting agents, pose severe global threats to pig health and industry economics. Conventional single-plex testing assays, whether relying on pathogen antigens or nucleic acids, exhibit limited efficacy in the face of co-infection events. The modern nucleic acid-based multiplex testing (NAMT) methods demonstrate substantial strengths in the simultaneous detection of multiple pathogens involving co-infections owing to their remarkable sensitivity, exceptional specificity, high-throughput, and short turnaround time. The development, commercialization, and application of NAMT assays in swine infectious disease surveillance would be advantageous for early detection and control of pathogens at the onset of an epidemic, prior to community transmission. Such approaches not only contribute to saving the lives of pigs but also aid pig farmers in mitigating or preventing substantial economic losses resulting from infectious disease outbreaks, thereby alleviating unwanted pressure on animal and human health systems. The current literature review provides an overview of some modern NAMT methods, such as multiplex quantitative real-time PCR, multiplex digital PCR, microarrays, microfluidics, next-generation sequencing, and their applications in the diagnosis of swine infectious diseases. Furthermore, the strengths and weaknesses of these methods were discussed, as well as their future development and application trends in swine disease diagnosis. Full article

Gastric cancer remains the fourth leading cause of cancer-related mortality worldwide. Advanced disease is associated with a poor prognosis, emphasizing the critical importance of early diagnosis through endoscopy. In addition to prognosis, disease extent also plays a pivotal role in guiding management strategies. Therefore, accurate locoregional staging (T and N staging) is vital for optimal prognostic and therapeutic planning. Endoscopic ultrasound (EUS) has long been an essential tool in this regard, with computed tomography (CT) and, more recently, positron emission tomography–computed tomography (PET–CT) serving as alternative imaging modalities. EUS is particularly valuable in the assessment of early gastric cancer, defined as tumor invasion confined to the mucosa or submucosa. These tumors are increasingly managed by endoscopic resection techniques offering improved post-treatment quality of life. EUS has also recently been utilized in the restaging process after neoadjuvant chemotherapy, aiding in the evaluation of tumor resectability and prognosis. Its performance may be further enhanced through the application of emerging techniques such as contrast-enhanced endosonography, EUS elastography, and artificial intelligence systems. In advanced, unresectable disease, complications such as gastric outlet obstruction (GOO) severely impact patient quality of life. In this setting, EUS-guided gastroenterostomy (EUS-GE) offers a less invasive alternative to surgical gastrojejunostomy. This review summarizes and critically analyzes the role of EUS in the context of gastric cancer, highlighting its applications across different stages of the disease and evaluating its performance relative to other diagnostic modalities. 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

Ocular diseases can significantly affect vision and overall quality of life, with diagnosis often being time-consuming and dependent on expert interpretation. While previous computer-aided diagnostic systems have focused primarily on medical imaging, this paper proposes VisionTrack, a multi-modal AI system for predicting multiple retinal diseases, including Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), drusen, Central Serous Retinopathy (CSR), and Macular Hole (MH), as well as normal cases. The proposed framework integrates a Convolutional Neural Network (CNN) for image-based feature extraction, a Graph Neural Network (GNN) to model complex relationships among clinical risk factors, and a Large Language Model (LLM) to process patient medical reports. By leveraging diverse data sources, VisionTrack improves prediction accuracy and offers a more comprehensive assessment of retinal health. Experimental results demonstrate the effectiveness of this hybrid system, highlighting its potential for early detection, risk assessment, and personalized ophthalmic care. Experiments were conducted using two publicly available datasets, RetinalOCT and RFMID, which provide diverse retinal imaging modalities: OCT images and fundus images, respectively. The proposed multi-modal AI system demonstrated strong performance in multi-label disease prediction. On the RetinalOCT dataset, the model achieved an accuracy of 0.980, F1-score of 0.979, recall of 0.978, and precision of 0.979. Similarly, on the RFMID dataset, it reached an accuracy of 0.989, F1-score of 0.881, recall of 0.866, and precision of 0.897. These results confirm the robustness, reliability, and generalization capability of the proposed approach across different imaging modalities. Full article

Introduction: Urinary tract infections (UTIs) remain a significant public health issue worldwide, particularly affecting the geriatric population with increased morbidity and mortality. Aging-related immune changes, comorbidities, and urogenital abnormalities contribute to the higher incidence and complexity of UTIs in elderly patients. Antimicrobial resistance, especially extended-spectrum beta-lactamase (ESBL) production and carbapenem resistance, poses a major challenge in managing UTIs in this group. Methods: This retrospective, multicenter study included 776 patients aged 65 and older, hospitalized with a diagnosis of urinary tract infection between January 2019 and August 2024. Clinical, laboratory, and microbiological data were collected and analyzed. Urine samples were obtained under sterile conditions and pathogens identified using conventional and automated systems. Antibiotic susceptibility testing was performed according to CLSI standards. Logistic regression analyses were conducted to identify factors associated with ESBL production, carbapenem resistance, and mortality. Results: Among the patients, the median age was 78.9 years, with 45.5% female. ESBL production was detected in 56.8% of E. coli isolates and carbapenem resistance in 1.2%. Klebsiella species exhibited higher carbapenem resistance (37.8%). Independent predictors of ESBL production included the presence of urogenital cancer and antibiotic use within the past three months. Carbapenem resistance was associated with recent hospitalization, absence of kidney stones, and infection with non-E. coli pathogens. Mortality was independently associated with intensive care admission at presentation, altered mental status, Gram-positive infections, and comorbidities such as chronic obstructive pulmonary disease and urinary incontinence. Discussion: Our findings suggest that urinary pathogens and resistance patterns in elderly patients are similar to those in younger adults reported in the literature, highlighting the need for age-specific awareness in empiric therapy. The identification of risk factors for multidrug-resistant organisms emphasizes the importance of targeted antibiotic stewardship, especially in high-risk geriatric populations. Multicenter data contribute to regional understanding of resistance trends, aiding clinicians in optimizing management strategies for elderly patients with UTIs. Conclusions: This study highlights that E. coli and Klebsiella species are the primary causes of UTIs in the elderly, with resistance patterns similar to those seen in younger adults. The findings also contribute important data on risk factors for ESBL production and carbapenem resistance, supported by a robust patient sample. Full article

Introduction: Artificial intelligence (AI) is increasingly being applied to improve the diagnosis and management of inflammatory bowel disease (IBD). Aims and Methods: We conducted a narrative review of the literature on AI applications in IBD endoscopy, focusing on diagnosis, disease activity assessment, therapy prediction, and detection of dysplasia. Results: AI systems have demonstrated high accuracy in assessing endoscopic and histological disease activity in ulcerative colitis and Crohn’s disease, with performance comparable to expert clinicians. Machine learning models can predict response to biologics and risk of complications. AI-assisted technologies like confocal laser endomicroscopy enable real-time histological assessment. Computer-aided detection systems improve identification of dysplastic lesions during surveillance. Challenges remain, including need for larger datasets, external validation, and addressing potential biases. Conclusions: AI has significant potential to enhance IBD care by providing rapid, objective assessments of disease activity, predicting outcomes, and assisting in dysplasia surveillance. However, further validation in diverse populations and prospective studies are needed before widespread clinical implementation. With ongoing advances, AI is poised to become a valuable tool to support clinical decision-making and improve patient outcomes in IBD. Addressing methodological, regulatory, and cost barriers will be crucial for the successful integration of AI into routine IBD management. Full article

Objective: The classification of skin cancer is very helpful in its early diagnosis and treatment, considering the complexity involved in differentiating AK from BCC and SK. These conditions are generally not easily detectable due to their comparable clinical presentations. Method: This paper presents a new approach to hyperspectral imaging for enhancing the visualization of skin lesions called the Spectrum-Aided Vision Enhancer (SAVE), which has the ability to convert any RGB image into a narrow-band image (NBI) by combining hyperspectral imaging (HSI) to increase the contrast of the area of the cancerous lesions when compared with the normal tissue, thereby increasing the accuracy of classification. The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM- Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. Results: The results demonstrated that SAVE enhanced classification performance and increased its accuracy, sensitivity, and specificity compared to a traditional RGB imaging approach. Conclusions: This advanced method offers dermatologists a tool for early and accurate diagnosis, reducing the likelihood of misclassification and improving patient outcomes. Full article

Background/Objectives: Current developments in computer-aided systems rely heavily on complex and computationally intensive algorithms. However, even a simple approach can offer a promising solution to reduce the burden on clinicians. Addressing this, we aim to employ unsupervised cluster analysis to identify prognostic subgroups of non-small-cell lung cancer (NSCLC) patients with brain metastasis (BM). Simple-yet-effective algorithms designed to identify similar group characteristics will assist clinicians in categorizing patients effectively. Methods: We retrospectively collected data from 95 NSCLC patients with BM treated at two oncology centers. To identify clinically distinct subgroups, two types of unsupervised clustering methods—two-step clustering (TSC) and hierarchical cluster analysis (HCA)—were applied to the baseline clinical data. Patients were categorized into prognostic classes according to the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA). Survival curves for the clusters and DS-GPA classes were generated using Kaplan–Meier analysis, and the differences were assessed with the log-rank test. The discriminative ability of three categorical variables on survival was compared using the concordance index (C-index). Results: The mean age of the patients was 61.8 ± 0.9 years, and the majority (77.9%) were men. Extracranial metastasis was present in 71.6% of the patients, with most (63.2%) having a single BM. The DS-GPA classification significantly divided the patients into prognostic classes (p < 0.001). Furthermore, statistical significance was observed between clusters created by TSC (p < 0.001) and HCA (p < 0.001). HCA showed the highest discriminatory power (C-index = 0.721), followed by the DS-GPA (C-index = 0.709) and TSC (C-index = 0.650). Conclusions: Our findings demonstrated that the TSC and HCA models were comparable in prognostic performance to the DS-GPA index in NSCLC patients with BM. These results suggest that unsupervised clustering may offer a data-driven perspective on patient stratification, though further validation is needed to clarify its role in prognostic modeling. Full article