Latest AI and machine learning research in radiology for healthcare professionals.
Multiplexed and ultrasensitive identification of foodborne pathogens is crucial for food safety. However, conventional methods are limited by complex matrices, insufficient signal conversion tools, and low sensitivity. Herein, we constructed bionic nanorobots by optimizing phenylboronic acid density on magnetic nanoparticles via polyethylene glycol linkers, achieving 92.21% Gram-positive bacteria ...
The emerging concept of "Green Radiology" aims to mitigate the environmental impact of medical imaging while maintaining high standards of patient care. Among various modalities, MRI is particularly resource-intensive. This review focuses on the clinical feasibility and significance of minimizing gadolinium-based contrast agent (GBCA) administration in neuroimaging, specifically for the longitudin...
Climate change is an important public health challenge, and healthcare itself contributes to greenhouse gas emissions. Within healthcare, radiology is...
To assess the value of convolutional neural network (CNN)-based denoising for the evaluation of non-calcified coronary plaques on ultrahigh-resolution...
Background and purpose Artificial intelligence (AI)-based tools for CT angiography (CTA) have been introduced to support rapid detection of large vess...
OBJECTIVE: Develop a multimodal model based on ultrasound (US) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data for predicting ...
Physics-based musculoskeletal models are often used to understand complex systems, like the human hand. Because generic hand models typically represen...
BACKGROUND: Vertebral compression fractures (VCFs) impose a substantial clinical and health care burden, and their management relies on timely access ...
BACKGROUND: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demons...
Alexithymia, defined by reduced emotional awareness, is prevalent in psychiatric and substance use disorders; however, its relationship with brain cir...
Splenic diseases in dogs and cats present significant diagnostic challenges, particularly in differentiating benign from malignant lesions using conve...
BACKGROUND: Sustained attention requires continuous engagement and is sensitive to individual differences in motivational processes. Variability in su...
INTRODUCTION: Current workflows for studying hydrocephalus in rodent models rely on manual segmentation or qualitative assessment of ventricular size ...
Structural lesions, including erosions, sclerosis, and pathological new bone formation, are key features of disease progression in axial spondyloarthr...
PURPOSE: Developing a deep learning model to simultaneously evaluate lymph node status and distinguish between benign and malignant breast masses has ...
OBJECTIVES: This study aimed to develop a clinical-radiomics model based on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) to improve...
Microvascular invasion (MVI) is a key prognostic factor in hepatocellular carcinoma but is currently only detectable after surgery. Here, we develop M...
We describe a publicly available, large, annotated dataset of 597 whole-body Positron Emission Tomography/Computed Tomography (PET/CT) studies with Pr...
Timely identification of children with ileocolic intussusception likely to fail air-enema reduction is critical to avoid delays and bowel perforation....
Efficient trauma assessment is essential for optimal patient care, with imaging playing a critical role in the detection of injuries. Rapid and accura...