Latest AI and machine learning research in radiology for healthcare professionals.
Purpose To develop a self-supervised text-vision framework to detect abnormalities on brain MRI scans by leveraging free-text neuroradiology reports, eliminating the need for expert-labeled training datasets. Materials and Methods This retrospective and prospective multicenter study included 81 936 brain MRI examinations and corresponding radiology reports for adult patients at two UK National Hea...
In this review, we highlight how artificial intelligence, specifically deep learning, is reshaping every aspect of cardiovascular magnetic resonance imaging: from planning and acquisition to reconstruction, analysis, and clinical report generation. We first introduce core machine learning paradigms and concepts, then survey recent deep learning advances to automate and enhance multiple aspects of ...
Electrical Impedance Tomography (EIT) provides a non-invasive, portable imaging modality with significant potential in medical and industrial applicat...
Artificial intelligence (AI) is rapidly transforming cardiac computed tomography (CT) imaging by enhancing image acquisition, reconstruction, and anal...
The introduction of foundational models, specifically large language models, has promised a health care transformation. However, the field is rapidly ...
A deep learning (DL) model was developed to generate contrast-enhanced MRI (CE-MRI) at multiple enhancement phases (arterial, portal venous, transitio...
Purpose To evaluate the performance of a deep learning algorithm (DLA) for detecting liver metastases (LM) in patients with colorectal cancer (CRC) ac...
Structural brain alterations have been observed in individuals with phenylketonuria (PKU); however, the potential impact of PKU on brain aging remains...
Cryo-electron tomography (cryo-ET) has emerged as the preferred technique for visualizing the organization of macromolecular complexes in situ and res...
Purpose To evaluate the generalizability of adult-trained models for hepatoblastoma segmentation to pediatric patients and to develop two deep learnin...
Purpose To evaluate the performance of deep learning models integrating multimodal data for predicting microvascular invasion (MVI) in hepatocellular ...
OBJECTIVES: Artificial intelligence (AI) applications are being increasingly explored in pain medicine due to AI's ability to handle multidimensional ...
Artificial intelligence (AI) is rapidly transforming the world, and medicine is at the forefront of this revolution. In cardiology, AI is increasingly...
MRI plays a central role in the diagnosis and management of multiple sclerosis (MS), and the 2017 revised McDonald criteria have improved the sensitiv...
BACKGROUND: Long axial field-of-view PET scanners are becoming increasingly available worldwide for clinical and research nuclear medicine examination...
BACKGROUND: Breath-hold PET imaging helps reduce respiratory motion artifacts in thoracoabdominal scans. However, its clinical application is limited ...
BACKGROUND: Progression independent of relapse activity (PIRA) contributes to long-term disability in multiple sclerosis (MS), even in early stages. H...
Metabolic liver diseases represent a growing global health concern with significant diagnostic and prognostic implications. Imaging offers non-invasiv...
ObjectiveThis review sought to systematize knowledge about the use of artificial intelligence in neurobiological research of mental disorders and asse...
Major depressive disorder (MDD) is a serious, complex psychiatric condition that affects millions of people worldwide. Early diagnosis and biomarker i...