Latest AI and machine learning research in radiology for healthcare professionals.
Myelin plays a critical role in the central nervous system, and its maturation is essential for understanding brain development. However, assessing myelin progression remains challenging due to variability across age groups. Radiologists typically rely on developmental atlases and age-based milestones, but manual evaluation is time-consuming and prone to inter-observer variability. This paper pres...
In 2018, Medicare established coverage and reimbursement for its first service using artificial intelligence (AI): computed tomography (CT) fractional flow reserve (FFRCT). FFRCT is used in conjunction with cardiac imaging to diagnose coronary artery disease. Medicare reimbursement provides the opportunity to observe clinicians' adoption of FFRCT and examine changes in utilization, spending, clini...
PURPOSE: To develop a deep learning-based auto-navigation technique for free-breathing golden-angle radial MRI named RANGR (Respiratory Auto-Navigator...
Quantitative susceptibility mapping (QSM) on MRI quantifies tissue magnetic susceptibility, which increases with iron accumulation, myelin loss, and n...
Our study investigates the effects of long-duration spaceflight on brain aging in spacefarers using structural MRI and machine learning models. Pre-, ...
Cytopathology, often abbreviated as cytology, has a central role in the early detection of cancer, such as cervical, lung and bladder cancers, owing t...
Image colorization transforms grayscale images into realistic color representations. It is a challenging area of research in computer vision due to un...
BACKGROUND: Reducing acquisition time in PET/CT imaging can degrade image quality and may compromise both diagnostic reliability and the robustness of...
BACKGROUND: At present, the early warning of difficult airway remains fraught with challenges. Previous ultrasonic quantitative parameters have demons...
BACKGROUND: With the rapid advancement of artificial intelligence (AI) in medical imaging, its application to coronary artery disease (CAD) imaging bi...
In recent years, the use of functional Magnetic Resonance Imaging (fMRI) methods to predict treatment response in schizophrenia (SCZ) through statisti...
The advent of X-ray multi-contrast imaging methods, providing absorption, phase, and dark-field images, holds tremendous promise for complementary and...
Real-time intra-operative brain tumour tissue analysis can reduce turnaround times and enable repeated sampling, enhancing diagnostic accuracy and gui...
Prostate cancer is among the most diagnosed malignancies in men worldwide and a leading cause of cancer-related mortality. Early and accurate diagnosi...
OBJECTIVES: Predicting seizure recurrence following a first unprovoked seizure (FUS) remains a significant clinical challenge, especially when routine...
Integrating resting-state functional magnetic resonance imaging (rs-fMRI) and phenotypic data is a promising way to build a comprehensive population g...
Ultrasound (US) imaging plays a crucial role in diagnosing heart and pelvic diseases, where sonographers tend to evaluate dynamic motion and structure...
BACKGROUND: Accurate non-invasive diagnosis of early-stage ovarian cancer remains challenging because of the limited number of biomarkers. Although ar...
Immune checkpoint inhibitors and anti-angiogenic targeted therapies have improved outcomes in hepatocellular carcinoma (HCC), but responses remain het...
Pathological and neuroimaging changes in the cerebellum of Alzheimer's disease (AD) patients have been well documented. However, the changes in cerebe...