Latest AI and machine learning research in radiology for healthcare professionals.
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.
Accurate segmentation of three-dimensional (3D) clinical medical images is critical for the diagnosis and treatment of spinal diseases. However, the complexity of spinal anatomy and the inherent uncertainties of current imaging technologies pose significant challenges for the semantic segmentation of spinal images. Although convolutional neural networks (CNNs) and Transformer-based models have ach...
Contrast-enhanced magnetic resonance lymphography (CE-MRL) plays a crucial role in preoperative diagnostic for evaluating tumor metastatic sentinel ly...
Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment lead to death, disabilities and impairment of the q...
OBJECTIVE: To validate high-frequency ultrasound as a valuable imaging modality in the assessment of the annular pulley system of the toes, describing...
OBJECTIVE: Hyaluronic acid (HA) can be degraded over time. However, the persistence of the effects after injection laryngoplasty (IL) for unilateral v...
BACKGROUND: Approximately 20-30Â % of patients with acute ischemic stroke due to lacunar infarction experience early neurological deterioration (END) w...
Medical image registration plays a crucial role in medical imaging, with a wide range of clinical applications. In this context, brain MRI registratio...
Sparse-view Computed Tomography (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging. Reconstructions based on the ...
Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and tr...
Magnetic resonance imaging (MRI)-derived hippocampus measurements have been associated with different cognitive domains. The knowledge of hippocampal ...
OBJECTIVE: Targeted blood-brain barrier (BBB) opening using focused ultrasound (FUS) and micro/nanobubbles is a promising method for brain drug delive...
We explore the potential of deep convolutional neural network (CNN) models for differential diagnosis of gout from musculoskeletal ultrasound (MSKUS)....
Coronary Heart Disease (CHD) is becoming a leading cause of death worldwide. To assess coronary artery narrowing or stenosis, doctors use coronary ang...
Robot-assisted endovascular intervention has the potential to reduce radiation exposure to surgeons and enhance outcomes of interventions. However, th...
BACKGROUND: Despite their exposure to potentially traumatic stressors, the majority of World Trade Center (WTC) responders-those who worked on rescue,...
Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are...
BACKGROUND: Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approache...
Intrahepatic cholangiocarcinoma (iCCA) and other subtypes of primary liver cancer (PLC) have overlapping clinical manifestations and radiological char...
OBJECTIVES: To create a placental radiomics-clinical machine learning model to predict FGR.