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.

Anatomy-centred deep learning improves generalisability and progression prediction in radiographic sacroiliitis detection.

Purpose: To examine whether incorporating anatomy-centred deep learning can improve generalisability and enable prediction of disease progression.

Methods: This retrospective multicentre study included conventional pelvic radiographs of four different patient cohorts focusing on axial spondyloarthritis collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets.

Elevation of spectral components of electrodermal activity precedes central nervous system oxygen toxicity symptoms in divers.

Background: Oxygen-rich breathing mixtures up to 100% are used in some underwater diving operations for several reasons. Breathing elevated oxygen partial pressures (PO) increases the risk of developing central nervous system oxygen toxicity (CNS-OT) which could impair performance or result in a seizure and subsequent drowning. We aimed to study the dynamics of the electrodermal activity (EDA) and heart rate (HR) while breathing elevated PO in the hyperbaric environment (HBO) as a possible means to predict impending CNS-OT.

Large Language Model Ability to Translate CT and MRI Free-Text Radiology Reports Into Multiple Languages.

Background High-quality translations of radiology reports are essential for optimal patient care. Because of limited availability of human translators with medical expertise, large language models (LLMs) are a promising solution, but their ability to translate radiology reports remains largely unexplored. Purpose To evaluate the accuracy and quality of various LLMs in translating radiology reports across high-resource languages (English, Italian, French, German, and Chinese) and low-resource languages (Swedish, Turkish, Russian, Greek, and Thai).

Leveraging Phase Information of 3D Isotropic Ultrashort Echo Time Pulmonary MRI for the Detection of Thoracic Lymphadenopathy: Toward an All-in-One Scan Solution.

Background: Ultrashort echo time (UTE) allows imaging of tissues with short relaxation times, but it comes with the expense of long scan times. Magnitude images of UTE magnetic resonance imaging (MRI) are widely used in pulmonary imaging due to excellent parenchymal signal, but have insufficient contrast for other anatomical regions of the thorax. Our work investigates the value of UTE phase images (UTE-Ps)-generated simultaneously from the acquired UTE signal used for the magnitude images-for the detection of thoracic lymph nodes based on water-fat contrast.