Latest AI and machine learning research in radiology for healthcare professionals.
Artificial intelligence (AI) holds immense promise in guiding clinical decision making in pediatric radiology, but its implementation in resource-constrained healthcare systems is limited by several significant challenges. The common AI methods, specifically deep learning models, used for image synthesis, reconstruction and segmentation require high-performance computers (HPC) and large memory cap...
OBJECTIVES: Deep learning (DL)-based image reconstruction (DLBIR) techniques promise accelerated MRI acquisitions with enhanced image quality. Herein, we compare the image quality of a DLBIR-based 3D FLAIR (3D-FLAIRDL) with conventional 3D FLAIR (3D-FLAIRSOC) in a cohort of multiple sclerosis (MS) patients. MATERIALS AND METHODS: Our prospective, reader-blinded study, included 26 MS patients who u...
BACKGROUND: Artificial intelligence-enhanced imaging techniques have demonstrated promising diagnostic potential for carotid plaques, a key cardiovasc...
BACKGROUND: Deep learning models have shown strong potential for automated fracture detection in medical images. However, their robustness under varyi...
Phase-contrast computed tomography (PCT) of the breast has previously been shown to produce higher-quality images at lower radiation doses without the...
This study aims to construct a predictive model for post-thrombectomy hemorrhagic transformation (HT) by integrating hemodynamic features derived from...
In a prior study we demonstrated the strong performance of convolutional neural networks (CNNs) in distinguishing healthy from glaucomatous eyes and s...
OBJECTIVES: To compare the radiologic assessment of Hirschsprung disease (HD) based on contrast enema with automated image analysis using a deep neura...
Physical exercise is known to delay the cognitive decline in the elderly. However, the effect of low-impact balance exercises such as yoga or Tai chi ...
Idiopathic pulmonary fibrosis (IPF) is a progressive disease of unknown aetiology, characterised by a radiological and/or morphological pattern of usu...
BACKGROUND: The recent declines in youth mental health highlight the need for research into the factors underlying distress and those that foster well...
BACKGROUND: This study aimed to demonstrate the feasibility of using computer vision (CV) to unobtrusively extract body motion metrics from videos of ...
BACKGROUND: Animal anatomy is revolutionised by use of digital techniques where it is implicated in research, education, and diagnostics. Modern compu...
OBJECTIVES: To evaluate the diagnostic interchangeability of DL-enhanced accelerated lumbar (L)-spine magnetic resonance imaging (MRI) with convention...
The depolymerization of polyethylene terephthalate (PET) through efficient chemical recycling remains a central challenge in plastic waste valorizatio...
BACKGROUND. Insights into the nature of false-positive findings flagged by contemporary mammography artificial intelligence (AI) systems could inform ...
Objective.Deformable registration plays a crucial role in motion estimation from a sequence of cardiac magnetic resonance (CMR) imaging, which is good...
Objective.Accurate and personalized radiation dose estimation is crucial for effective targeted radionuclide therapy (TRT). Deep learning (DL) holds p...
Triboelectric nanogenerators (TENGs) hold great promise as self-powered sensors, but their practical applications are limited by charge leakage at hig...
INTRODUCTION: Oral potentially malignant disorders (OPMDs) can lead to oral cancer, which is one of the most common cancers worldwide. Prevention is c...