Latest AI and machine learning research in radiology for healthcare professionals.
BACKGROUND: Medulloblastoma (MB) is the most common malignant brain tumor in children. Current diagnostic methods, such as MRI and lumbar puncture, are invasive and not sensitive enough, making early diagnosis challenging. MicroRNAs (miRNAs) have emerged as promising biomarkers for cancer diagnosis due to their dysregulated expression in tumors. This study aims to develop a novel machine learning ...
OBJECTIVE: This study aimed to investigate the role of volumetric and dissemination parameters obtained from pretreatment 18-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) in predicting progression/relapse in patients with diffuse large B-cell lymphoma (DLBCL) with machine learning algorithms.
OBJECTIVES: One of the most promising auxiliaries for screening breast cancer (BC) is ultrasound (US) radio-frequency (RF) time series. It has the sup...
This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step a...
BACKGROUND AND OBJECTIVES: Distinguishing neurodegenerative diseases is a challenging task requiring neurologic expertise. Clinical decision support s...
Breast cancer (BC), the most common cancer in women, remains a global health concern. According to the World Health Organization, in the year 2022, BC...
PURPOSE: Radiology reports are essential for communicating imaging findings to guide diagnosis and treatment. Although most radiology reports are accu...
Coronary CT Angiography (CCTA) is essential for assessing atherosclerosis and coronary artery disease, aiding in early detection, risk prediction, and...
PURPOSE: Automated detection and segmentation of surgical devices, such as catheters or wires, in X-ray fluoroscopic images have the potential to enha...
BACKGROUND AND PURPOSE: Identifying amyloid-beta (Aβ)-positive patients is essential for Alzheimer's disease (AD) clinical trials and disease-modifyin...
BACKGROUND: For magnetic resonance imaging (MRI), a short acquisition time and good image quality are incompatible. Thus, reconstructing time-resolved...
BACKGROUND AND PURPOSE: Diagnosis of tumefactive demyelination can be challenging. The diagnosis of indeterminate brain lesions on MRI often requires ...
PURPOSE: The C-arm biplane imaging system, designed for cerebral angiography, detects pathologies like aneurysms using dual rotating detectors for hig...
BACKGROUND: Magnetic resonance imaging (MRI) is a crucial tool for visualizing orbital structures and detecting eye pathologies. However, manual segme...
OBJECTIVE: To review the latest developments in self-supervised deep learning (DL) techniques for magnetic resonance imaging (MRI) reconstruction, emp...
Fast and accurate organ-at-risk (OAR) and gross tumor volume (GTV) contour propagation methods are needed to improve the efficiency of magnetic resona...
BACKGROUND: Two of the most common complaints seen in neurology clinics are Alzheimer's disease (AD) and mild cognitive impairment (MCI), characterize...
An external validation of IAIA-BL-a deep-learning based, inherently interpretable breast lesion malignancy prediction model-was performed on two patie...
AIMS: Obesity is associated with functional alterations in the brain. Although spatial organisation changes in the brains of individuals with obesity ...
Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neurosci...