A Deep Learning Ensemble Approach for Automated COVID-19 Detection from Chest CT Images.

Background: The aim of this study was to evaluate the performance of an automated COVID-19 detection method based on a transfer learning technique that makes use of chest computed tomography (CT) images.

Method: In this study, we used a publicly available multiclass CT scan dataset containing 4171 CT scans of 210 different patients. In particular, we extracted features from the CT images using a set of convolutional neural networks (CNNs) that had been pretrained on the ImageNet dataset as feature extractors, and we then selected a subset of these features using the Information Gain filter.

Non Vitamin K Antagonist Oral Anticoagulants in Atrial Fibrillation Patients Scheduled for Electrical Cardioversion: A Real-Life Propensity Score Matched Study.

Aim: The aim of the present study was to assess the safety and effectiveness of non-vitamin K antagonist oral anticoagulants (NOACs) versus vitamin K antagonists (VKAs) in atrial fibrillation (AF) patients undergoing electrical cardioversion (EC).

Methods: A propensity score-matched analysis was performed in order to identify two homogeneous groups including AF patients on NOACs and VKAs treatment scheduled for EC. The primary safety endpoint was major bleeding.