No abstract

The unexpected observation of severe pulmonary tuberculosis after a 7-month combined pegylated interferon-ribavirin for chronic hepatitis C, prompted us to search an eventual immunodeficiency (lymphopenia and/or depletion of CD4+ T-lymphocytes. The retrieval of a chest radiograph incidentally performed 11 y before and showing a probable primary tuberculosis, paralleled a negligible clinical history. The enlargement of interferon indications needs careful evaluation for prior (usually missed) tuberculosis, to prevent or avoid its possible reactivation. Latent tuberculosis is increasingly reported because of extended life expectancy, immigration, and recent availability of cure for multiple chronic disorders, which are often borne by primary-secondary immunodeficiency.

Electrical cardioversion of recent-onset AF in the SOU is safe, effective and reduces hospitalisations. Further studies are needed to identify the most cost-effective strategy for the management of AF patients in emergency settings.

We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models: a quality assessment module that automates predictions of image quality, and a diagnosis assistance module that determines the likelihood-of-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training the quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Using more than 25,000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86% when classifying between sufficient and insufficient quality images by the quality assessment module. For data of sufficient qualityas determined by the quality assessment modulethe mean classification accuracy, sensitivity, and specificity in detecting COVID-19-positive cases were 0.95, 0.91, and 0.97, respectively, across five holdout test data sets unseen during the training of any networks within the proposed system. Overall, the integration of the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at pointof-care.