Background Deep learning (DL), a subset of artificial intelligence (AI), has been applied to
pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been …

Due to its widespread availability, low cost, feasibility at the patient's bedside and
accessibility even in low-resource settings, chest X-ray is one of the most requested …

Background Patients presenting to the emergency department (ED) with acute chest pain
(ACP) syndrome undergo additional testing to exclude acute coronary syndrome (ACS) …

Objectives The aim of this study was to investigate the feasibility and impact of a novel deep
learning superresolution algorithm tailored to partial Fourier allowing retrospectively …

Purpose The detection of pleural effusion in chest radiography is crucial for doctors to make
timely treatment decisions for patients with chronic obstructive pulmonary disease. We used …

Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning
systems to assist clinicians and improve interpretation accuracy. An understanding of the …

Purpose To develop two deep learning-based systems for diagnosing and localizing
pneumothorax on portable supine chest X-rays (SCXRs). Methods For this retrospective …

Objectives To investigate the potential and limitations of utilizing transformer-based report
annotation for on-site development of image-based diagnostic decision support systems …

Background Ultrasonic for detecting and evaluating pleural effusion is an essential part of
the Extended Focused Assessment with Sonography in Trauma (E‐FAST) in emergencies …

Background Radiology requests and reports contain valuable information about diagnostic
findings and indications, and transformer-based language models are promising for more …