Radiology

Environmental Exposures and Neuroimaging in Children with Neurodevelopmental Disorders: A Scoping Review.

PURPOSE OF REVIEW: Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have been linked to environmental exposures, yet the underlying neurobiological mechanisms remain poorly understood. Magnetic resonance imaging (MRI) offers an important in vivo tool for examining how environmental neurotoxicants impact brain development...

Deep-learning Saturation Transfer Magnetic Resonance Fingerprinting (ST-MRF) in Patients with Parkinson's Disease.

Parkinson's disease (PD) is marked by progressive neurodegeneration in the substantia nigra (SN). This study evaluated deep-learning saturation-transfer magnetic resonance fingerprinting (ST-MRF) to quantify molecular and microstructural changes in PD. We examined 23 patients with PD (PwPD) and 22 matched healthy controls (HCs) using multimodal imaging, including ST-MRF. ST-MRF detected significan...

Bridging the Gap Between Artificial Intelligence and Clinical Readiness in Endometriosis Diagnosis: A Systematic Review.

OBJECTIVE: To systematically evaluate the methodological quality and diagnostic performance of artificial intelligence (AI) applications, specifically...

Prediction Models of Polycyclic Aromatic Hydrocarbons in Urban Road Dust via Magnetic Properties, Particle Size, and Urban Environmental Features.

This study explores the novel application of artificial neural networks to predict polycyclic aromatic hydrocarbons (PAHs) pollution in urban road dus...

Artificial intelligence-driven early screening and diagnosis of pancreatic cancer: technical innovations, clinical applications, and precision medicine strategies.

BACKGROUND: Pancreatic cancer is characterized by prolonged subclinical progression, molecular heterogeneity, and late clinical presentation, resultin...

Computational and machine learning approach for nanoparticle-enhanced bio-heat transport with coupled effects of interparticle spacing and nanoparticles radius.

Targeted delivery of drugs and hyperthermia in cardiovascular disease demand the accurate delivery of nanoparticles in complex arterial geometries. Th...

Clinical evaluation of deep learning accelerated 3D magnetic resonance cholangiopancreatography at 1.5 T and 3 T.

OBJECTIVE: Routine clinical 3D magnetic resonance cholangiopancreatography (MRCP) is typically performed either as lower resolution breath-hold (BH) a...

Fluoroscopic image-driven deep learning model for predicting intussusception irreducibility during air enema in children.

BACKGROUND: Accurate identification of irreducible intussusception during air enema is crucial for optimizing enema strategies. Current methods are li...

Evaluating AI-powered instruction on Computed Tomography Angiography (CTA) teaching outcomes: a systematic review and meta-analysis.

OBJECTIVE: To systematically evaluate the effectiveness of AI-assisted teaching in computed tomography angiography (CTA) training, focusing on theoret...

Identification and verification of worst-case radiological transport scenarios for Ireland: A simulation-based approach to nuclear emergency preparedness (2011-2024).

This study presents a comprehensive simulation-based assessment of potential transboundary radiological transport to Ireland from six nuclear faciliti...

The challenge of bringing advances in sacroiliac joint imaging to the clinic.

PURPOSE OF REVIEW: Recently reported criteria are more stringent for classification of axial spondyloarthritis (axSpA) in the absence of positive imag...

Development of an Algorithm to Estimate Fat-Free Mass to Optimize Contrast Injection for Computed Tomography Imaging of the Liver.

OBJECTIVES: Contrast-enhanced computed tomography (CT) is central to liver imaging. Inadequate enhancement can compromise diagnostic accuracy and impa...

Global trends and hotspots of machine learning in the diagnosis of prostate cancer: a bibliometric analysis from 1997 to 2024.

PURPOSE: Prostate cancer (PCa) diagnosis has advanced with the integration of machine learning (ML). This study analyzes global trends in ML-based PCa...

Identifying diagnostic biomarkers in functional motor disorders through multimodal behavioral, neurophysiological, and imaging assessment using explainable machine learning.

BACKGROUND: Functional motor disorders (FMDs) represent a frequent and disabling neurological condition. The lack of reliable diagnostic biomarkers an...

Attention-based Multimodal Spatiotemporal Enhanced Interaction Network For Major Depressive Disorder Detection.

Although deep learning models have shown promising results in detecting major depressive disorder (MDD), two main limitations remain: insufficient exp...

Referenceless Proton Resonance Frequency Thermometry Using Deep Learning with Self-Attention.

Proton resonance frequency (PRF) MR thermometry provides temperature feedback for MR-guided focused ultrasound (FUS), but conventional baseline-refere...

Magnetic Resonance Neurography: Evolution, Technical Foundations, and Future Directions.

ABSTRACT: In the past 20 years, magnetic resonance neurography has evolved from an experimental technique into an essential diagnostic pillar for peri...

Assessing osteosarcopenia using automated multi-structure-segmentation and Hounsfield unit-based analysis of lumbar muscles and vertebral bodies on computed tomography.

Sarcopenia and osteoporosis are prevalent age-related conditions that substantially increase the risk of falls, fractures, and functional decline. Thi...

Machine learning outperforms large language models for survival prediction in advanced hepatocellular carcinoma: a multicenter study.

Accurate prognostic prediction remains a critical unmet need in advanced hepatocellular carcinoma (HCC). While machine learning (ML) models have demon...

Imaging of ewing sarcoma: an updated analysis including presenting features, prognostic imaging biomarkers, and treatment response assessment.

Ewing sarcoma is a highly aggressive small round cell sarcoma primarily affecting children and adolescents. Imaging plays a central role from diagnosi...