Latest AI and machine learning research in radiology for healthcare professionals.
BACKGROUND: Dystonia in children is a heterogeneous condition with variable response to deep brain stimulation (DBS). Brain-age gap, a machine learning-derived metric of structural deviation from norm, may capture signatures that differentiate underlying biotypes and predict outcomes. METHODS: A brain age model was trained on several thousand normative developmental trajectories (n = 2623). Brain-...
BACKGROUND: Mechanical thrombectomy (MT) is the standard treatment for acute posterior circulation artery occlusion (PCAO), but predicting outcomes remains challenging. Existing prognostic models combine clinical, imaging, and procedural variables but show inconsistent performance. Our objective is to evaluate the diagnostic accuracy of Machine Learning (ML) models in predicting favorable function...
BACKGROUND: Kidney stones are a prevalent urological condition with significant global burden, often diagnosed using ultrasound (US) as a first-line m...
OBJECTIVE: To develop an interpretable multimodal model that integrates pre-treatment Magnetic resonance imaging (MRI)-based deep learning radiomics (...
Varicocele is a common cause of male infertility, with ultrasound (US) serving as the primary diagnostic tool. Current practice relies on manual, subj...
Spine magnetic resonance imaging is among the most frequently performed examinations in clinical radiology and places substantial demands on workflow ...
Cardiovascular disease (CVD) remains the leading cause of death among women globally, with significant mortality and poorer outcomes compared with men...
BACKGROUND AND AIMS: Women are underdiagnosed and undertreated for cardiovascular disease (CVD). Automatic quantification of breast arterial calcifica...
Neuroimaging, particularly magnetic resonance imaging (MRI), has become a cornerstone in elucidating the neural underpinnings of Major Depressive Diso...
Accurately segmenting spinal structures from magnetic resonance imaging (MRI) is essential for diagnosing degenerative disc diseases. However, 1.5 T l...
Recent advancements in nuclear medicine, particularly in personalised radiopharmaceutical therapy, have emphasised the growing need for precise assess...
Thirty percent of interval breast cancers, diagnosed between routine screening mammograms, have a poorer prognosis than screen-detected cancers. Deep ...
BACKGROUND AND AIMS: Conventional biomarkers such as low-density lipoprotein (LDL) and high-density lipoprotein may fail to identify patients' risk fo...
OBJECTIVE: Artificial intelligence tools show promise in fracture detection but may be impaired by hidden stratification. We aim to evaluate the diagn...
BACKGROUND: Proton density fat fraction (PDFF) measured using magnetic resonance imaging (MRI) is considered a noninvasive reference measure of fat de...
BACKGROUND: Although deep learning reconstruction (DLR) has been shown to improve image quality in MRI, its impact on quantitative physiologic paramet...
RATIONALE AND OBJECTIVES: Accurate preoperative assessment of axillary lymph node (ALN) status and nodal burden is crucial for individualized manageme...
PURPOSE: To develop and evaluate Crohn-BOOST, an open-source tool enabling semi-automatic segmentation of intestinal lesions and creeping fat on magne...
Accurate segmentation of anatomical structures in cardiac magnetic resonance imaging (MRI) plays an irreplaceable role in the clinical management of c...
BACKGROUND: ST-segment elevation myocardial infarction (STEMI) requires rapid, accurate electrocardiogram (ECG) interpretation. The diagnostic effecti...