Latest AI and machine learning research in radiology for healthcare professionals.
Currently, bone cancer remains a big challenge in healthcare, early and accurate diagnosis is therefore key to achieving the required treatment outcomes. To this end, this research attempts to present a novel hybrid framework, i.e. TriMedNet, which works to classify bone cancer using multi-modal data sources. The diagnostic model is derived from integrating imaging data (MRI scans), unstructured c...
PURPOSE: We review burnout risk factors in interventional radiology (IR) and explore how artificial intelligence (AI) would address burnout from a workplace aspect. MATERIALS AND METHODS: We performed a literature search on PubMed on risk factors for burnout in interventional radiology and AI tools to address burnout challenges. RESULTS: IR specialists face burnout risk at personal, workplace and ...
Radiologically Isolated Syndrome (RIS) is characterized by incidental MRI findings indicative of multiple sclerosis (MS) in asymptomatic individuals. ...
Digital Subtraction Angiography (DSA) is one of the gold standards for vascular disease diagnosis. With the help of a contrast agent, time-resolved 2D...
Efforts to define biologically grounded subtypes of schizophrenia have increasingly leveraged neuroimaging data and clustering algorithms. Such approa...
PURPOSE: To improve the imaging efficiency of 3D carotid simultaneous noncontrast angiography and intraplaque hemorrhage (SNAP) MRI by reconstruction ...
BACKGROUND: MRI is important for cardiac disease evaluation, but accurate diagnosis remains challenging in less experienced centers. Although large la...
RATIONALE AND OBJECTIVES: Timely radiology access is essential for accurate diagnosis, treatment planning, and efficient care delivery. U.S. academic ...
BACKGROUND AND PURPOSE: Current literature on deep learning-accelerated intracranial vessel wall imaging has been largely limited to postprocessing-ba...
OBJECTIVES: This study investigates whether tooth volume measurements derived from postmortem computed tomography (PMCT) can provide discriminatory in...
BACKGROUND AND OBJECTIVES: Traditional prognostication after aneurysmal subarachnoid hemorrhage depends on subjective clinical grading and radiologica...
Brain fog has raised significant public health concerns as a common neurocognitive impairment in the post-COVID-19 condition, involving memory loss, p...
OBJECTIVE: Interventional procedures expose physicians to scattered radiation, particularly to their upper extremities, posing occupational health ris...
Tuberculosis (TB) remains a leading infectious cause of morbidity and mortality worldwide, and major diagnostic and therapeutic challenges persist des...
OBJECTIVES: Deep learning-based synthetic contrast imaging has been proposed as an alternative to iodinated and gadolinium-based contrast agents in CT...
BACKGROUND: Young patients with acute coronary syndrome (ACS) exhibit diverse demographic, clinical and angiographic characteristics. We hypothesized ...
Medical image segmentation is challenging due to the diversity of medical images and the lack of labeled data, which motivates recent developments in ...
Improving risk stratification for coronary artery disease (CAD), the leading global cause of death, remains a daily challenge in clinical practice. Th...
PURPOSE: To describe Cornea Anterior Segment Dataset from Moorfields via INSIGHT (CADMUS), a large multimodal anterior segment imaging dataset develop...
PURPOSE: To compare accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction (DLR-TSE) with conventional T2-weighted TSE (con...