In this article, we evaluate the effectiveness of Cone Beam Computed Tomography, through a case study, in assessing the complication of intracranial bleeding during an endovascular treatment of brain arteriovenous malformation when compared to Multislice-Detector Computed Tomography performed immediately after the procedure. The image quality of Cone Beam Computed Tomography has enough diagnostic value in differentiating between haemorrhage, embolic materials and the arteriovenous malformation nidus to facilitate physicians to decide for further management of the patient.
Introduction: Renal size measurement using ultrasound is a valuable parameter in the diagnosis of renal function and its diseases. This study is aimed to determine the differences of mean and correlation between the renal length (RL), renal width (RW) and renal parenchymal thickness (RPT) with age, gender and anthropometric measurements among indigenous population in Malaysia. Methods: A prospective cross-sectional study was carried out in this sur- vey. Abdominal sonography was performed on 240 subjects. Sonography of the renal size included measurements of RL, RW and RPT. A portable ultrasound machine (Mindray DP-50, Shenzen, China) with a 3.5 MHz convex probe was used in this study. An independent-samples t-test, one-way ANOVA and Pearson's correlation coefficient test
were performed in statistical analysis. Data were analyzed using SPSS program version 22.0. A P-value of
Tuberculosis (TB) remains a significant global health challenge, characterized by high incidence and mortality rates on a global scale. With the rapid advancement of computer-aided diagnosis (CAD) tools in recent years, CAD has assumed an increasingly crucial role in supporting TB diagnosis. Nonetheless, the development of CAD for TB diagnosis heavily relies on well-annotated computerized tomography (CT) datasets. Currently, the available annotations in TB CT datasets are still limited, which in turn restricts the development of CAD tools for TB diagnosis to some extent. To address this limitation, we introduce DeepPulmoTB, a CT multi-task learning dataset explicitly designed for TB diagnosis. To demonstrate the advantages of DeepPulmoTB, we propose a novel multi-task learning model, DeepPulmoTBNet (DPTBNet), for the joint segmentation and classification of lesion tissues in CT images. The architecture of DPTBNet comprises two subnets: SwinUnetR for the segmentation task, and a lightweight multi-scale network for the classification task. Furthermore, to enhance the model's capacity to capture TB lesion features, we introduce an improved iterative optimization algorithm that refines feature maps by integrating probability maps obtained in previous iterations. Extensive experiments validate the effectiveness of DPTBNet and the practicality of the DeepPulmoTB dataset.