Latest AI and machine learning research in radiology for healthcare professionals.
This study built a model to forecast the maturity of lungs by blending radiomics and deep learning m...
. Positron emission tomography (PET) is affected by statistical noise due to constraints on tracer d...
Photon-counting computed tomography (PCCT) marks a significant advancement over conventional Energy-...
Enlarged fetal heart conditions may indicate congenital heart diseases or other complications, makin...
White light endoscopy is the clinical gold standard for detecting diseases in the gastrointestinal...
Medical image segmentation models are often trained on curated datasets, leading to performance de...
Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feas...
Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imag...
Osteoarthritis (OA) is a prevalent and disabling chronic disease, with knee OA being the most common...
The increasing global burden of allergic diseases and multimorbidity underscores the urgent need for...
BACKGROUND: Hepatocellular carcinoma (HCC) is a major global contributor to cancer-related mortality...
Esophageal cancer (EC), a common malignant tumor of the digestive tract, requires early diagnosis an...
SNOMED CT is a comprehensive controlled biomedical ontology widely used as an information exchange s...
The integration of artificial intelligence (AI) into healthcare is revolutionising the industry by e...
We developed and validated a magnetic resonance imaging (MRI)-based radiomics model for the classifi...
BACKGROUND: Only in recent years it has been demonstrated that the thoracolumbar fascia is involved ...
Over the past decades, computer-aided diagnosis tools for breast cancer have been developed to enh...
Deep learning models have shown promise in lung pathology detection from chest X-rays, but widespr...
The left atrium (LA) plays a pivotal role in modulating left ventricular filling, but our comprehe...
High-resolution slice-to-volume reconstruction (SVR) from multiple motion-corrupted low-resolution...
In imaging inverse problems, we would like to know how close the recovered image is to the true im...