Latest AI and machine learning research in radiology for healthcare professionals.
PURPOSE: MRI detection of subtle focal cortical dysplasia (FCD)-like abnormalities remains challenging in focal epilepsy. Higher signal-to-noise ratio and spatial resolution offered by ultra-high-field 7T MRI and surface-based graph-neural-network (GNN) analysis may improve detection of subtle cortical abnormalities. We evaluated whether combining 7T MRI with a surface-based GNN classifier improve...
PURPOSE: To evaluate detectability of hypodense hepatic and renal lesions on portal venous phase CT reconstructed with deep learning image reconstruction (DLIR) versus adaptive statistical iterative reconstruction-V at 40% strength (ASIR-V 40) in patients with large body habitus. METHODS: This single-center retrospective study included patients ≥ 90 kg who underwent abdominal CT (February- May 202...
PURPOSE: To examine the impact of artificial intelligence (AI) on radiologists' workload and economic outcomes by synthesizing current evidence on wor...
OBJECTIVE: Artificial intelligence (AI) demonstrates significant potential in medical imaging diagnosis, yet its real-world clinical value requires va...
RATIONALE AND OBJECTIVES: Zero echo time (ZTE) is an advanced MRI technique providing CT-like images of mineralized tissues. This study evaluates the ...
BACKGROUND AND PURPOSE: While intravesical Bacillus Calmette-Guérin Vaccine (BCG) instillation remains standard adjuvant therapy for high-grade non-mu...
Deep learning (DL) methods increasingly outperform classical approaches in brain MRI analysis, yet their generalizability across independent imaging c...
BACKGROUND: Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are 2 of the leading causes of vision loss worldwide. As population a...
Multi-modal models that fuse neuroimaging with clinical assessment data represent the current state of the art for automated Alzheimer's disease detec...
[18F]FDG PET is entering a new phase shaped by changes in representation, validation, and clinical integration. Beyond regional interpretation, networ...
BACKGROUND: GM1 gangliosidosis is an inherited, progressive, and fatal neurodegenerative lysosomal storage disorder with no approved treatment. In thi...
BACKGROUND: To develop a combined model integrating intratumoral and peritumoral delta-radiomics from multi-parametric MRI with clinical features for ...
BACKGROUND: While adjacent segment degeneration (ASDeg) is a major complication following lumbar fusion, objective tools for preoperative risk predict...
OBJECTIVES: Deep-learning (DL)-accelerated MRI can significantly reduce acquisition times. Studies evaluating interchangeability with conventional 3D ...
Growing evidence indicates that disruption of the microbiota-gut-brain (MGB) axis is a key factor in autism spectrum disorder (ASD), affecting neurode...
BACKGROUND: Degenerative cervical myelopathy (DCM) is the leading cause of spinal cord impairment worldwide, yet conventional magnetic resonance imagi...
Active surveillance (AS) is widely used for men with low-risk and selected favorable intermediate-risk prostate cancer, but pathways remain heterogene...
BACKGROUND AND PURPOSE: Alzheimer's disease, a common type of dementia, gradually steals memories and impacts daily life as brain cells deteriorate. W...
PURPOSE: A deep learning algorithm for contrast amplification in brain MRI, trained exclusively on adult data, was tested for cross-population general...
Federated Learning (FL) emerged as a privacy-preserving paradigm for collaborative training of deep learning models across institutions without sharin...