Latest AI and machine learning research in radiology for healthcare professionals.
BACKGROUND: Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression is essential for guiding targeted therapy in breast cancer. Conventional immunohistochemistry and fluorescence in situ hybridization remain the diagnostic standard but are invasive, costly, and limited by sampling bias. PURPOSE: To develop and internally evaluate an explainable deep learning model based o...
BACKGROUND: Early identification of individuals at risk of dementia is essential for preventive care and timely enrolment into disease-modifying interventions. However, most existing prediction approaches rely on invasive, costly, or research-only biomarkers that are not scalable within public healthcare systems. Routinely acquired National Health Service (NHS) brain magnetic resonance imaging (MR...
BACKGROUND: The objective of this study was to characterise the agreement of the CE-certified automated robotic ultrasound system ARTHUR v.2.0, combin...
Computed tomography colonography, also known as virtual colonoscopy, is a minimally invasive imaging technique developed in the early 1990s to evaluat...
BACKGROUND: Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging...
With the rapid advancement of multi-detector computed tomography (MDCT) and image post-processing technologies, CT angiography (CTA) has become a corn...
BACKGROUND: Manual interpretation of echocardiographic data for strain analysis is time-consuming and prone to inter-observer variability. With the ad...
Microscopic magnetic resonance imaging (μMRI) is a versatile, non-invasive imaging modality and a potential candidate for studying the internal biomec...
BACKGROUND: Coronary artery disease (CAD) progression has been examined mainly in cohorts enriched for major adverse cardiovascular events (MACE), a h...
INTRODUCTION: Obstructive sleep apnea syndrome (OSAS) is a highly prevalent condition, particularly among at-risk populations such as patients with ob...
Cardiac sarcoidosis (CS) is a clinically heterogeneous disorder associated with significant morbidity and mortality, including heart failure, conducti...
The Brain Imaging and Neurophysiology Dataset (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositories...
Accurate quantification of spindle-shaped cells in bright-field microscopy remains challenging due to low contrast, noise, and highly variable cell mo...
Accurate recognition and classification of severity level of brain tumor (BT) is essential for clinical decision making. Manual assessment of brain tu...
Dysphagia presents a serious risk of aspiration that requires continuous monitoring. This study introduces standardized 2 s voice segments for aspirat...
Deep learning models for brain tumor diagnosis often lack interpretability beyond qualitative visual heatmaps. Clinicians require not only tumor local...
BACKGROUND: Imaging plays a fundamental and increasing role in the diagnostic work-up of pediatric patients. Non-invasive imaging methods include ultr...
BACKGROUND: Commercial artificial intelligence (AI) software for cardiac magnetic resonance (CMR) analysis has shown promising internal validation res...
Conventional prediction models incorporating genetic and clinical factors including breast density underperform in non-European populations. We invest...
OBJECTIVES: We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG positron emission tomography...