PURPOSE: To assess the efficacy of radiomics features extracted from non-contrast computed tomography (NCCT) scans in differentiating multiple etiologies of spontaneous intracerebral hemorrhage (ICH).
PURPOSE: The time-intensive nature of acquiring 3D T1-weighted MRI and analyzing brain volumetry limits quantitative evaluation of brain atrophy. We explore the feasibility and reliability of deep learning-based accelerated MRI scans for brain volume...
PURPOSE: The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist's accuracy in identifying aneurysms and reduce...
OBJECTIVE: Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) a...
UNLABELLED: Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60-69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. However, ...
PURPOSE: To verify the effectiveness of artificial intelligence-assisted volume isotropic simultaneous interleaved bright-/black-blood examination (AI-VISIBLE) for detecting brain metastases.
PURPOSE: To evaluate nnU-net's performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI.
PURPOSE: To assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction.
PURPOSE: Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, rad...