By fusing the multiscale brain connectome data, the multichannel deep neural network model improved attention deficit hyperactivity disorder detection performance considerably compared with the use…
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Purpose: To retrospectively assess outcome in patients with clinically unsuspected pulmonary embolism (PE) at chest multi–detector row computed tomography (CT). Materials and Methods: Institutional review…
“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and…
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Background Clinical decision making and drug development for fibrostenosing Crohn disease is constrained by a lack of imaging definitions, scoring conventions, and validated end points.…
Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications…
Background Standardized methods to measure and describe Crohn disease strictures at CT enterography are needed to guide clinical decision making and for use in therapeutic…
Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in…
Background Radiology practices have a high volume of unremarkable chest radiographs and artificial intelligence (AI) could possibly improve workflow by providing an automatic report. Purpose…
Background Lutetium 177 [177Lu]Lu-PSMA-617 (177Lu-PSMA-617) is a prostate-specific membrane antigen (PSMA)–targeted radioligand therapy for metastatic castration-resistant prostate cancer (mCRPC). Quantitative PSMA PET/CT analysis could provide…
