Latest AI and machine learning research in radiology for healthcare professionals.
Increased right ventricular (RV) radiotracer uptake on perfusion imaging has been recognized as a marker of increased cardiovascular risk. However, this uptake is challenging to quantify because of the variable intensity of uptake in a thin structure. We used a validated artificial intelligence-enhanced method for segmenting the right ventricle from CT attenuation correction (CTAC) imaging to auto...
Although deep learning models have improved individual PET analysis, image processing, and quantification tasks, end-to-end automation from raw DICOM data to quantitative clinical reporting remains limited, particularly in heterogeneous real-world settings. Methods: As a proof-of-concept, an autonomous large language model (LLM)-orchestrated multitool agent for end-to-end PET/CT interpretation was...
Accurate identification of cerebrovascular stenosis is essential for early stroke prevention and effective clinical management. Magnetic resonance ang...
This study aimed to compare deep learning models based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) whole-tumor and habitat regio...
PURPOSE: To make an overview of the (1) diagnostic tools and (2) diagnostic criteria for MIH and HSPM. METHODS: This systematic review was registered ...
OBJECTIVES: To evaluate the performance of Chat Generative Pre-Trained Transformer-4 Omni (ChatGPT-4o) in answering multimodal critical care board rev...
Accurate quantitative assessment of temporal bone microanatomy is essential for otologic research and surgical planning. However, existing measurement...
PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...
Reading the Herculaneum papyri is challenging because both the scrolls and the ink, which is carbon-based, are carbonized. In X-ray radiography and to...
BACKGROUND: Transformer-based architectures have rapidly gained prominence in medical imaging due to their ability to model long-range dependencies an...
OBJECTIVES: To investigate the performance of an artificial intelligence (AI) diagnostic system for thyroid nodule sonography based on deep learning c...
BACKGROUND: Accurate preoperative prediction of isocitrate dehydrogenase (IDH) genotype in gliomas is crucial for treatment planning and prognostic ev...
BACKGROUND: Preoperative cardiovascular risk stratification is essential in noncardiac surgery, but conventional testing is frequently overused, incre...
INTRODUCTION: To develop a novel deep learning framework for automated classification and detection of fetal intracranial structures in first-trimeste...
INTRODUCTION: Contrast-enhanced CT is central to oncological imaging, yet no official guidelines exist for contrast injection protocols. As a result, ...
Accurate estimation of spatially heterogeneous conductivity distributions is essential for reliable electric field modeling in non-invasive brain stim...
Artificial intelligence (AI) is transforming segmentation tasks in radiotherapy, but model reliability remains a critical concern, particularly for tu...
OBJECTIVE: Depth-of-interaction (DOI) information is essential for improving the spatial resolution in positron emission tomography (PET) imaging. Exi...
OBJECTIVES: To compare the acquisition time, image quality, and diagnostic confidence of DL-accelerated DIR (DIR-DL) with conventional MRI in patients...
BACKGROUND AND PURPOSE: To develop a two-stage framework that combines deep learning-based super-resolution with subsequent image processing to genera...