Explore the vast range of titles available.

Background/Objectives: Age-related macular degeneration (AMD) is the leading cause of visual impairment among the elderly. Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that enables detailed visualisation of retinal vascular layers. However, clinical assessment of OCTA images is often challenging due to high data volume, pattern variability, and subtle abnormalities. This study aimed to develop automated algorithms to detect and quantify AMD in OCTA images, thereby reducing ophthalmologists’ workload and enhancing diagnostic accuracy. Methods: Two texture-based algorithms were developed to classify OCTA images without relying on segmentation. The first algorithm used whole local texture features, while the second applied principal component analysis (PCA) to decorrelate and reduce texture features. Local texture descriptors, including rotation-invariant uniform local binary patterns (LBP2riu), local binary patterns (LBP), and binary robust independent elementary features (BRIEF), were combined with machine learning classifiers such as support vector machine (SVM) and K-nearest neighbour (KNN). OCTA datasets from Manchester Royal Eye Hospital and Moorfields Eye Hospital, covering healthy, dry AMD, and wet AMD eyes, were used for evaluation. Results: The first algorithm achieved a mean area under the receiver operating characteristic curve (AUC) of 1.00±0.00 for distinguishing healthy eyes from wet AMD. The second algorithm showed superior performance in differentiating dry AMD from wet AMD (AUC 0.85±0.02). Conclusions: The proposed algorithms demonstrate strong potential for rapid and accurate AMD diagnosis in OCTA workflows. By reducing manual image evaluation and associated variability, they may support improved clinical decision-making and patient care.

Background/Objectives: Glioblastoma (GBM) is an aggressive and lethal primary brain tumor with a poor prognosis, with a 5-year survival rate of approximately 5%. Despite advances in oncologic treatments, including surgery, radiotherapy, and chemotherapy, survival outcomes have remained stagnant, largely due to the failure of conventional therapies to address the tumor’s inherent heterogeneity. Radiomics, a rapidly emerging field, provides an opportunity to extract features from MRI scans, offering new insights into tumor biology and treatment response. This study evaluates the potential of delta radiomics, the study of changes in radiomic features over time in response to treatment or disease progression, exploring the potential of delta radiomics to track temporal radiation changes in tumor morphology and microstructure. Methods: A cohort of 50 female CD1 nude mice was injected intracranially with G7 glioblastoma cells and divided into irradiated (IR) and non-irradiated (non-IR) groups. MRI scans were performed at baseline (week 11) and post-radiation (weeks 12 and 14), and radiomic features, including shape, histogram, and texture parameters, were extracted and analyzed to capture radiation-induced changes. The most robust features were those identified through intra-observer reproducibility assessment, ensuring reliability in feature selection. A machine learning model was developed to classify irradiated tumors based on delta radiomic features, and statistical analyses were conducted to evaluate feature feasibility, stability, and predictive performance. Results: Our findings demonstrate that delta radiomics effectively captured significant temporal variations in tumor characteristics. Delta radiomics features exhibited distinct patterns across different time points in the IR group, enabling machine learning models to achieve a high accuracy. Conclusions: Delta radiomics offers a robust, non-invasive method for monitoring the treatment of glioblastoma (GBM) following radiation therapy. Future research should prioritize the application of MRI delta radiomics to effectively capture short-term changes resulting from intratumoral radiation effects. This advancement has the potential to significantly enhance treatment monitoring and facilitate the development of personalized therapeutic strategies.

Different hospitals in Sudan detected a rare condition of liver vascular abnormalities characterized by vascular outflow impairment. The study aimed to describe the common radiological features of Budd Chiari syndrome and determine which feature is most frequently employed to characterize this condition during imaging techniques, primarily contrast-enhanced computed tomography (CECT) scans. The study was conducted between March 2023 and June 2024 at Kuwaiti Specialized Hospital (KSH) and other diagnostic centers using a liver protocol on a CT machine (Optima 520 GE-CT machine, 16 slices). The study was a retrospective, cross-sectional, and review-based analysis of a rare study type carried out to characterize the state of BCS in Sudan; the CT scan's findings on the liver, HVs, IVC, and abdomen were carefully assessed. The age range of the 61 patients who underwent a successful triphasic CT abdomen for the liver was 2-78 years. The findings indicate that: the majority of patients 57.4% were male, the most common age groups were 39-52 years old, and the mean age at diagnosis was 45 years. BCS is primarily caused by hepatic veins (HVs) thrombosis, which is observed in 18.03% of cases, and HVs are not seen in 55.73% of cases. Liver parenchymal enhancement appears heterogenous in 27.87%, while heterogeneously enlarged liver was seen in 24.59%, and cirrhotic in 14.75% of BCS patients. In comparison, 59.01 percent of BCS patients arrived without varices. Varices were observed in the splenorenal and gastroesophageal regions in 37.7% of cases. Ascites accounted for the majority of BCS complications 73.77%, with SM vein blockage and squeezed duodenum accounting for 3.27% of each complication. The likelihood of developing ascites increases with age, and it is most common in patients between the ages of 39 and 52 years. Patient age had the greatest effect on the development of ascites. The common features of BCS as revealed by contrast-enhanced CT of the liver are non-visible HVs, venous occlusion at either level of HVs or IVC, caudate lobe enlargement, heterogenous, normal or enlarged liver, collateral venous varices at the splenorenal and gastroesophageal region, and ascites.

The early and precise identification of Alzheimer’s Disease (AD) continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases. This study presents a novel Deformable Attention Vision Transformer (DA-ViT) architecture that integrates deformable Multi-Head Self-Attention (MHSA) with a Multi-Layer Perceptron (MLP) block for efficient classification of Alzheimer’s disease (AD) using Magnetic resonance imaging (MRI) scans. In contrast to traditional vision transformers, our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions, markedly diminishing processing demands while improving localized feature representation. DA-ViT contains only 0.93 million parameters, making it exceptionally suitable for implementation in resource-limited settings. We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories, achieving a test accuracy of 80.31%, a macro F1-score of 0.80, and an area under the receiver operating characteristic curve (AUC) of 1.00 for the Mild Demented category. Thorough ablation studies validate the ideal configuration of transformer depth, headcount, and embedding dimensions. Moreover, comparison research indicates that DA-ViT surpasses state-of-the-art pre-trained Convolutional Neural Network (CNN) models in terms of accuracy and parameter efficiency.