The aim of this article is to utilize ultrasound to evaluate the normal cross-sectional area (CSA)of the vagus nerve (VN) in the carotid sheath. This study included 86 VNs in 43 healthy subjects (15 men, 28 women); mean age 42.1 years and mean body mass index 26.2 kg/m². For each subject, the bilateral VNs were identified by US at the anterolateral neck within the common carotid sheaths. One radiologist obtained 3 separate CSA measurements for each of the bilateral VNs with complete transducer removal between each measurement. Additionally, for each participant, demographic information of age and gender as well as body mass index, weight, and height were documented. The mean CSA of the right VN in the carotid sheath was 2.1 and 1.9 mm² for the left VN. The right VN CSA was significantly larger than the left VN (P < .012). No statistically significant correlation was noted in relation to height, weight, and age. We believe that the reference values for the normal CSA of the VN obtained in our study, could help in the sonographic evaluation of VN enlargement, as it relates to the diagnosis of various diseases affecting the VN.

Objectives: Uterine cancer originates from the cells lining the uterus and can develop through abnormal cell growth, potentially leading to damage in surrounding tissues and the formation of precancerous cells. Early detection significantly improves prognosis. Despite advancements in deep learning-based diagnostic methods, challenges remain, including the dependence on expert input and the need for more accurate classification models. This study aims to address these limitations by proposing a novel and efficient methodology for diagnosing uterine cancer using an integrated deep learning pipeline optimized through a nature-inspired algorithm.Methods: This study introduces the Whale Optimization Algorithm-based Ensemble Network (WOAENet), a deep learning pipeline that classifies uterine MRI into three classes: malignant, benign, and normal. The Whale Optimization Algorithm (WOA) is used to fine-tune the hyperparameters of three deep learning models: MobileNetV2, DenseNet121, and a lightweight vision model (LVM). Each model is trained with its optimized settings, and its outputs are combined using a Soft Voting Ensemble method that calculates the average of the predicted probabilities to arrive at the final classification.Results: The WOAENet framework was evaluated using a uterine cancer MRI dataset obtained from King Abdullah University Hospital. Our proposed model outperformed standard pre-trained models across several performance metrics. It achieved an accuracy of 88.57%, a specificity of 94.29%, and an F1 score of 88.54%, indicating superior performance in diagnosing uterine cancer.Conclusion: WOAENet demonstrates a high level of accuracy and reliability in classifying uterine MRI images, marking a significant advancement by utilizing a novel dataset. The findings support the potential of AI-driven approaches in enhancing the diagnosis and treatment of gynecological conditions, paving the way for more accessible and accurate clinical tools.

Gadolinium toxicity has been accompanied by side effects among patients scanned with MRI, especially patients with chronic renal insufficiency. The toxicity, pharmacokinetics, and pharmacodynamics of gadolinium-based contrast agents intact blood-brain barriers accumulate in the brain. This study aimed to estimate the awareness about the side effects of gadolinium-enhanced MRI scans among Saudi non-radiologists to improve and raise the level of awareness of all physicians about the side effects of gadolinium-enhanced MRI studies among patients. This improvement will be due to our clarification of the most important issues related to gadolinium contrast in MRI, by illustrating the uses and the major side effects of this contrast. Additionally, we want to find a method that will help with raising awareness of gadolinium toxicity and alert the stakeholders and the head of radiology departments about the need of creating and implementing new official regulations to minimize the abuse of enhanced MRI studies. This is a cross-sectional study conducted among non-radiological doctors in Saudi Arabia. A questionnaire based on a literature review was developed and distributed among non-radiological doctors through an online platform. The questionnaire included basic demographic data and a behavioral and awareness assessment about gadolinium. All statistical analyses were carried out using Statistical Package for the Social Sciences (SPSS) IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp. 460 non-radiological doctors contributed, 65% males vs. 35% females. The most commonly known side effect of gadolinium was acute pancreatitis (92.8%), followed by encephalopathy (90%) and arrythmias (88.9%). Awareness of gadolinium toxicity among the non-radiological doctors was poor in 74.6%, 20.9% were moderate, and only 4.6% were classified into a good awareness level. The factor associated with an increased level of awareness was being an internal medicine doctor (p=0.006). The awareness level of non-radiological doctors about gadolinium toxicity was suboptimal. The knowledge of internal medicine physicians was better, but the other specialties need more education. As most of the physicians were not exposed to patients’ adverse reactions, this could be one of the reasons why they have a lack of knowledge about the subject. On the other hand, appropriate patient screening and sufficient prophylactic measures can prevent adverse events. Therefore, in knowledge, understanding, and practice, it is important to come up with the most effective response to any gadolinium contrast adverse events.

Introduction: The segmentation of brain tumor MRI images is one of the most challenging tasks because of the variability and complexity associated with tumor tissues. This study introduces PoSAM-ULTRA, an improved segmentation framework designed to enhance the accuracy and robustness of brain tumor segmentation. Methods: PoSAM-ULTRA employs the Polar-Bear Foraging Optimisation (PBFO) algorithm for hyperparameter tuning and utilizes an improved Segment Anything Model as its backbone. The framework is based on a ResNet-34 encoder modified to accept a four-channel input (RGB + prior information). Multi-scale feature extraction is performed via DownBlocks, while discriminative feature learning is enhanced using the Convolutional Block Attention Module (CBAM). Attention Gates are incorporated to ensure effective skip connections, and a multistage decoder is used for robust upsampling and feature integration. The model was evaluated on a dataset from the Integrative Genomic Analysis of Diffuse Lower Grade Gliomas (LGG) and compared with UNet, UNet++, and nnUNet. Results: The proposed PoSAM-ULTRA model outperformed the baseline models, achieving superior performance with a Dice score of 91.4%, IoU of 88.9%, Accuracy of 99.8%, Precision of 95.2%, and Recall of 93.3%. Discussion: The obtained results demonstrate the robustness and reliability of PoSAM-ULTRA in handling the complexity of brain tumor MRI segmentation, highlighting its effectiveness for challenging medical image segmentation tasks.

The aim of this study is to assess the ability of ultrasound to identify and estimate the normal cross-sectional area (CSA) of the great auricular nerve (GAN) at a defined landmark in healthy adults. The study sample included 80 GANs in 40 participants (11 males, 29 females), mean age 36.5 years, mean height 160.1 cm, mean weight 63 kg, mean body mass index 24.4 kg/m ² . Two radiologists separately obtained sonographic images of the bilateral GANs. Each participant was scanned 3 times bilaterally to assess for intraobserver reliability. The mean CSA of the bilateral GANs was 0.8 mm ² (range 0.4–2.7 mm ² ). The GAN CSA positively correlated with weight, height, and body mass index. This study confirms the ability of ultrasound to reliably assess the GAN at a defined landmark and estimate its normal CSA. Knowledge of how to identify and measure the GAN would be expected to help decrease complications during procedures and help establish normal reference values.