Latest AI and machine learning research in radiology for healthcare professionals.
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT)...
The distinction between Xanthogranulomatous Cholecystitis (XGC) and Gallbladder Carcinoma (GBC) is challenging due to their similar imaging features. This study aimed to differentiate between XGC and GBC using a deep learning nomogram model built from contrast enhanced computed tomography (CT) scans. 297 patients were included with confirmed XGC (94) and GBC (203) as the training and internal vali...
BACKGROUND: Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a...
The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machin...
Spirulina platensis biopigments have been documented as a potential source of nutritional, physiological, and pharmacological purposes due to the pres...
Key unmet needs in multiple sclerosis (MS) include detection of early pathology, disability worsening independent of relapses, and accurate monitoring...
This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC s...
Percutaneous coronary intervention (PCI) has increasingly become the main treatment for coronary artery disease. The procedure requires high experienc...
Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine ...
Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important...
OBJECTIVE: At the time of informed consent (IC) for coronary angiography (CAG), patients' knowledge of the process is inadequate. Time constraints and...
OBJECTIVE: To examine the clinical outcomes of optical coherence tomography (OCT)-guided percutaneous coronary intervention (PCI) in patients presenti...
The value of circulating tumor DNA (ctDNA) as a biomarker of disease activity in classic Hodgkin lymphoma (cHL) patients has not yet been well establi...
Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal in...
Gesture recognition based on surface electromyography (sEMG) has been widely used in the field of human-machine interaction (HMI). However, sEMG has l...
Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events-the leading cause of global mortality-have known li...
BACKGROUND: In recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical ...
BACKGROUND: Point-of-care lung ultrasound (LUS) allows real-time patient scanning to help diagnose pleural effusion (PE) and plan further investigatio...
For more than 2 decades since the first imaging procedure was performed in a living patient, intravascular optical coherence tomography (OCT), with it...
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, i...