Objectives Double reading in diagnostic radiology can find discrepancies in the original
report, but a systematic program of double reading is resource consuming. There are …

Purpose To use meta-analysis to determine the discrepancy rate when interpreting
computed tomography (CT) studies performed in adult patients and to determine whether …

Background Identification of large vessel occlusion (LVO) is critical to the management of
acute ischemic stroke and prerequisite to endovascular therapy in recent trials. Increasing …

Please see the Editorial Comment by Ibrahim S. Tuna discussing this article. Chinese
(audio/PDF) and Spanish (audio/PDF) translations are available for this article's abstract …

Purpose To (1) develop a deep learning system (DLS) using a deep convolutional neural
network (DCNN) for identification of pneumothorax,(2) compare its performance to first-year …

Background and Purpose Medical errors can result in significant morbidity and mortality. The
goal of our study is to evaluate correlation between shift volume and errors made by …

BACKGROUND AND PURPOSE: Diagnostic errors affect 2%–8% of neuroradiology studies,
resulting in significant potential morbidity and mortality. This retrospective analysis of a large …

Approximately 4% of radiologic interpretation in daily practice contains errors and
discrepancies that should occur in 2–20% of reports. Fortunately, most of them are minor …

To evaluate the ability of a commercialized deep learning reconstruction technique to depict
intracranial vessels on the brain computed tomography angiography and compare the …

Background Artificial intelligence (AI) models in real-world implementation are scarce. Our
study aimed to develop a CT angiography (CTA)-based AI model for intracranial aneurysm …