Latest AI and machine learning research in radiology for healthcare professionals.
Contrast-induced nephropathy (CIN) is an important cause of acute kidney injury following exposure t...
Microfibers are recognized as the most prevalent form of microplastics, with a widespread distributi...
BackgroundThe impact of deep learning (DL)-based computed tomography (CT) reconstruction on the visu...
OBJECTIVE: Rib fractures are common yet time-consuming to diagnose. This study explores automation v...
OBJECTIVES: To evaluate the performance of an optimized deep-learning-based algorithm (AI) for the d...
In recent years, gastric oral contrast ultrasonography (OCUS) and double contrast-enhanced ultrasono...
INTRODUCTION: To explore the feasibility of an ultrasound radiomics machine learning model based on ...
Background: Radiotherapy (RT) is a cornerstone of multimodal treatment for rectal cancer (RC); yet, ...
OBJECTIVE: Quantitative ultrasound tomography faces challenges in reconstructing speed‑of‑sound (SoS...
OBJECTIVE: This study aims to address the slow reconstruction speed of iterative reconstruction algo...
OBJECTIVE: Quantitative Susceptibility Mapping (QSM) is a magnetic resonance imaging technique t...
Minimally invasive spine surgery (MISS), supported by advancements in endoscopic systems, tubular re...
Left ventricular ejection fraction (LVEF) is a critical parameter in the evaluation of cardiac funct...
INTRODUCTION: MRI is commonly used to evaluate pelvic musculoskeletal infections. Limited "quick" MR...
Planetary interiors experience high-pressure-high temperature conditions that give rise to unconvent...
Radiological protocol selection is a critical but time-consuming step in clinical workflow, requirin...
Magnetic resonance imaging (MRI) plays a pivotal role in the diagnostic work-up of dementia. In addi...
BACKGROUND: Accurate preoperative assessment of lymph node metastasis (LNM) is crucial for treatment...
OBJECTIVE: This study aims to develop and validate ensemble learning models based on pre-rupture or ...
We present TLPath, a deep learning framework that predicts bulk-tissue telomere length from tissue m...
OBJECTIVE: To investigate the utility of a machine learning model based on MRI radiomics in predicti...