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Background Challenges in the diagnosis of obstructive jaundice include locating the level of obstruction, knowing the cause of obstruction, and differentiating between benign and malignant causes. Imaging plays a significant role in detecting the causes of obstruction. Radiologists aim to diagnose biliary obstruction, its level, extent, and probable causes to determine the appropriate treatment for each case. Methods Our study is a retrospective medical record review study. It included 150 patients who had ultrasound (US) diagnosis of biliary obstruction and underwent magnetic resonance cholangiopancreatography (MRCP) or endoscopic retrograde cholangiopancreatography (ERCP) in King Fahad Specialist Hospital, Buraidah. The patients' medical records have been reviewed to measure the sensitivity and specificity of US, MRCP, and ERCP. Results Statistical analysis of the data showed that the sensitivity of US in detecting the most common cause of biliary obstruction, common bile duct (CBD) stone, was 26.6%, while the specificity was 100%. Comparing this sensitivity of US in detecting CBD stones to that of MRCP and ERCP, we obtained the following: US, 26.6%; MRCP, 62.9%; and ERCP, 62.4%. Although US was the least sensitive for detecting CBD stones, its specificity in this detection was 100%, while MRCP was 63.6%, and ERCP was 55.2%. Conclusion US is the best initial step for the diagnosis of biliary obstruction. However, MRCP and ERCP are more sensitive in detecting CBD stones compared to US. Also, compared to US, they have shown higher percentages in all aspects of detection: level, cause, and extent of biliary obstruction.

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Community detection is a crucial challenge in social network analysis. This task is important because it gives leads to study emerging phenomena. Indeed, it makes it possible to identify the different communities representing individuals with common interests and/or strong connections between them. In addition, it allows tracking the transformation of these communities over time. In this work, we propose a dynamic community detection approach called Attributes, Structure, and Messages distribution-based approach (ASMsg). In addition to the node attributes and the topological structure of the network, we use the rate of transferred messages as the key concept of this approach. Therefore, we obtain communities with similar members that are strongly connected and also frequently interacting. Furthermore, the proposed approach is able to detect all possible communities' transformations even if the communities are overlapped. To demonstrate its efficiency, we widely test ASMsg on artificial and real-world dynamic networks and compare it with representative methods. The results show the superiority of our approach in terms of detected communities.

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