Latest AI and machine learning research in radiology for healthcare professionals.
Radiomics allows extraction from medical images of quantitative features that are able to reveal tis...
Deep learning has been used extensively for medical image analysis applications, assuming the traini...
SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insi...
PURPOSE: Artificial intelligence (AI) has the potential to improve diagnostic imaging on multiple le...
Breast cancer is one of the most common malignancies among women globally. Magnetic resonance imagin...
Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the p...
AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model fo...
The dorsal root ganglion (DRG) contains all primary sensory neurons, but its functional role in soma...
BACKGROUND AND PURPOSE: Deep learning (DL)-based reconstruction enables improving the quality of MR ...
RATIONALE AND OBJECTIVES: To construct and validate an interpretable machine learning (ML) radiomics...
BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (...
BACKGROUND: Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of canc...
In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical...
We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging imp...
Magnetic resonance imaging (MRI) is a non-invasive imaging technique that provides high soft tissue ...
Anomalous aortic origin of the coronary artery (AAOCA) is a rare cardiac condition that can lead to ...
BACKGROUND: Microvascular invasion (MVI) is an important risk factor for early postoperative recurre...
This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analys...
RATIONALE AND OBJECTIVES: Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose d...
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis f...
Exploring the clinical significance of employing deep learning methodologies on ultrasound images fo...