AI Medical Compendium Journal:
Diagnostic and interventional imaging

Showing 41 to 50 of 82 articles

Artificial intelligence in adrenal imaging: A critical review of current applications.

Diagnostic and interventional imaging
In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps f...

Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to assess the impact of the new artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and radiation dose compared with iterative reconstruction algorithm in lumbar spine comput...

Artificial intelligence in emergency radiology: A review of applications and possibilities.

Diagnostic and interventional imaging
Artificial intelligence (AI) applications in radiology have been rising exponentially in the last decade. Although AI has found usage in various areas of healthcare, its utilization in the emergency department (ED) as a tool for emergency radiologist...

Added value of an artificial intelligence solution for fracture detection in the radiologist's daily trauma emergencies workflow.

Diagnostic and interventional imaging
PURPOSE: The main objective of this study was to compare radiologists' performance without and with artificial intelligence (AI) assistance for the detection of bone fractures from trauma emergencies.

Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence.

Diagnostic and interventional imaging
PURPOSE: Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and t...

A deep learning tool without muscle-by-muscle grading to differentiate myositis from facio-scapulo-humeral dystrophy using MRI.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to assess the capabilities of a deep learning (DL) tool to discriminate between type 1 facioscapulo-humeral dystrophy (FSHD1) and myositis using whole-body muscle magnetic resonance imaging (MRI) examination wit...

Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to evaluate a deep-learning model (DLM) for classifying coronary arteries on coronary computed tomography -angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS).

External validation of a commercially available deep learning algorithm for fracture detection in children.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to conduct an external validation of a fracture assessment deep learning algorithm (Rayvolve®) using digital radiographs from a real-life cohort of children presenting routinely to the emergency room.