AI Medical Compendium Journal:
Clinical radiology

Showing 11 to 20 of 109 articles

Introduction and accuracy assessment of Nicolab's StrokeViewer in a developing stroke thrombectomy UK service. a service development/improvement project.

Clinical radiology
AIM: The aim of this study was to evaluate the implementation of artificial intelligence (AI) software in a quaternary stroke centre as well as assess the accuracy and efficacy of StrokeViewer software in large vessel occlusion detection and its pote...

Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage.

Clinical radiology
AIMS: To investigate radiology staff perceptions of an AI tool for chest radiography triage, flagging findings suspicious for lung cancer to expedite same-day CT chest examination studies.

Deep learning constrained compressed sensing reconstruction improves high-resolution three-dimensional (3D) T2-weighted turbo spin echo magnetic resonance imaging (MRI) of the lumbar spine.

Clinical radiology
AIM: We sought to assess the image quality of three-dimensional (3D) T2-weighted (T2w) turbo spin echo (TSE) sequences with deep learning (DL)-constrained compressed sensing (CS) reconstruction relative to a reference two-dimensional (2D) T2w TSE seq...

Facilitating the use of routine data to evaluate artificial intelligence solutions: lessons from the NIHR/RCR data curation workshop.

Clinical radiology
Radiology currently stands at the forefront of artificial intelligence (AI) development and deployment over many other medical subspecialities within the scope of both research and clinical practice. Given this current leadership position, it is impe...

Enhancing diagnosis: ensemble deep-learning model for fracture detection using X-ray images.

Clinical radiology
AIM: Orthopedic trauma results in the injury of bone joints and tendons of the body. A radiologist reviews and monitors large numbers of radiographs daily, which can lead to the diagnostic error. Therefore, there is a need to automate the detection o...

Deep learning-based computer-aided detection of ultrasound in breast cancer diagnosis: A systematic review and meta-analysis.

Clinical radiology
PURPOSE: The aim of this meta-analysis was to assess the diagnostic performance of deep learning (DL) and ultrasound in breast cancer diagnosis. Additionally, we categorized the included studies into two subgroups: B-mode ultrasound diagnostic subgro...

Low energy virtual monochromatic CT with deep learning image reconstruction to improve delineation of endoleaks.

Clinical radiology
AIM: This study aimed to investigate the utility of low-energy virtual monochromatic imaging (VMI) combined with deep-learning image reconstruction (DLIR) in improving the delineation of endoleaks (ELs) after endovascular aortic repair (EVAR) in cont...