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Uller et al [3] reported 5 cases of bile duct obliteration under fluoroscopic guidance, whereby the bile duct was first visualized percutaneously and then Onyx was applied for obliteration via a microcatheter. The decisive reason to use CT guidance was that the suspected fistula was located directly under the resection surface and that the feeding isolated bile duct was very small, not allowing for a clear anatomical classification. [Extracted from the article]

This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging. A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction. Basic demographics (age and sex) and initial vital parameters (O 2 -saturation, respiratory rate, and body temperature) were recorded. Generalized mixed models were used to calculate the accuracy of vital parameters for detection of COVID-19 and to evaluate the diagnostic accuracy of CT. A clinical score based on vital parameters, age, and sex was established to estimate the pretest probability of COVID-19 and used to define low, intermediate, and high risk groups. A p -value of <0.05 was considered statistically significant. The sole use of vital parameters for the prediction of COVID-19 was inferior to CT. After correction for confounders, such as age and sex, CT showed a sensitivity of 0.86, specificity of 0.78, and positive predictive value of 0.36. In the subgroup analysis based on pretest probability, positive predictive value and sensitivity increased to 0.53 and 0.89 in the high-risk group, while specificity was reduced to 0.68. In the low-risk group, sensitivity and positive predictive value decreased to 0.76 and 0.33 with a specificity of 0.83. The negative predictive value remained high (0.94 and 0.97) in both groups. The accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability of COVID-19. • A simple score to estimate the pre-test probability of COVID-19 can be calculated using vital parameters, basic vital parameters. • Accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability. • In patients with mild symptoms, CT examinations are unlikel unlikely to be effective with a very low positive predictive value. [ABSTRACT FROM AUTHOR]

Graphical overview illustrating the application of AI-based techniques in analyzing MRI data for improved diagnosis and predictive modeling in the field of rheumatology. This review refers to applications for rheumatoid arthritis, spondyloarthritis, myopathy and systemic sclerosis. Parts of this figure were created with BioRender.com.▪ Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management. [ABSTRACT FROM AUTHOR]