Comparison of dark-field chest radiography and CT for the assessment of COVID-19 pneumonia.

Background: Dark-field chest radiography allows the assessment of the structural integrity of the alveoli by exploiting the wave properties of x-rays.

Purpose: To compare the qualitative and quantitative features of dark-field chest radiography in patients with COVID-19 pneumonia with conventional CT imaging.

Materials And Methods: In this prospective study conducted from May 2020 to December 2020, patients aged at least 18 years who underwent chest CT for clinically suspected COVID-19 infection were screened for participation.

Defining Developmental Language Disorder and Dyslexia in Schools: A Mixed-Methods Analysis.

Purpose: Interprofessional practice requires regular communication between professionals from different disciplines using shared terminology. Within schools, many professionals are tasked with supporting children with language disorders, namely, developmental language disorder (DLD) and/or dyslexia. Limited information exists as to (a) how school-based professionals' definitions of DLD and dyslexia align with research definitions, (b) how different school-based professionals define language disorders, (c) how school-based professionals' definitions of DLD and dyslexia align across professional groups, and (d) how one's definition of a language disorder correlates with other measures of knowledge.

Simulated low-dose dark-field radiography for detection of COVID-19 pneumonia.

Background: Dark-field radiography has been proven to be a promising tool for the assessment of various lung diseases.

Purpose: To evaluate the potential of dose reduction in dark-field chest radiography for the detection of the Coronavirus SARS-CoV-2 (COVID-19) pneumonia.

Materials And Methods: Patients aged at least 18 years with a medically indicated chest computed tomography scan (CT scan) were screened for participation in a prospective study between October 2018 and December 2020.

Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging.

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7194 contrast-enhanced CT images (3597 with COPD; 3597 healthy controls) from 78 subjects were selected retrospectively (01.2018-12.