The prognostic relevance of pleural effusion in patients with COVID-19 - A German multicenter study.

Purpose: This study evaluates the prognostic significance of pleural effusion (PE) in COVID-19 patients across thirteen centers in Germany, aiming to clarify its role in predicting clinical outcomes.

Methods: In this retrospective analysis within the RACOON project (Radiological Cooperative Network of the COVID-19 pandemic), 1183 patients (29.3 % women, 70.

Real-world federated learning in radiology: hurdles to overcome and benefits to gain.

Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles.

Pericardial Effusion Predicts Clinical Outcomes in Patients with COVID-19: A Nationwide Multicenter Study.

Rationale And Objectives: The prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of PE in patients with Covid 19 in a large multicentre setting.

Materials And Methods: This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic).

Every CFTR variant counts - Target-capture based next-generation-sequencing for molecular diagnosis in the German CF Registry.

Background: In times of genotype guided therapy options, a total of 3.2 % of people with CF (pwCF) in the German CF Registry[1] only have one or no CFTR-variant detected after genetic analysis. Additionally, genetic data in the Registry can be documented as free text and can therefore be prone to error.

Metadata-independent classification of MRI sequences using convolutional neural networks: Successful application to prostate MRI.

Purpose: The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data.