NEWS AND PRESS

Prostate cancer is a leading cause of cancer-related mortality among men worldwide. Biparametric magnetic resonance imaging (bpMRI) plays a crucial role in early detection and evaluation of prostate lesions. Artificial intelligence (AI) has emerged as a promising tool for optimizing diagnostic workflows. This study aimed to assess the effectiveness of the commercial AI software Mediaire© in identifying suspicious prostate lesions on bpMRI, comparing its performance with interpretations by experienced radiologists and evaluating its potential to enhance early prostate cancer detection through concordance with human evaluations.

Multiple sclerosis (MS) is a chronic autoimmune disorder that leads to neurodegeneration and progressive neurological impairment. A key imaging feature of MS is the presence of demyelinating brain lesions, which vary in distribution and size. This study aimed to assess the performance and clinical relevance of AI-based MRI analysis for lesion detection and brain atrophy quantification in patients with MS.

Objectives: To quantify the clinical value of integrating a commercially available artificial intelligence (AI) algorithm for intracranial aneurysm detection in a screening setting that utilizes cranial magnetic resonance imaging (cMRI) scans acquired primarily for other clinical purposes.

In Multiple Sclerosis (MS) cerebral MRI is essential for disease and treatment monitoring. For this purpose, software solutions are available to support the radiologist with image interpretation and reporting of follow up imaging. Aim of this study was to evaluate an AI based software for longitudinal lesion detection with clinical data and to determine the influence of different MRI machines in such setting.

This study evaluates the clinical value of a deep learning–based artificial intelligence (AI) system that performs rapid brain volumetry with automatic lobe segmentation and age- and sex-adjusted percentile comparisons.

This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the reference standard, while contextualizing its performance within the framework of PI-RADS v2.1 criteria.

The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist’s accuracy in identifying aneurysms and reduces image analysis time.

Multiple Sclerosis (MS) is a common autoimmune disease of the central nervous system. MRI plays a crucial role in diagnosing as well as in disease and treatment monitoring. Therefore, evaluation of cerebral MRI of MS patients is part of daily clinical routine. A growing number of companies offer commercial software to support the reporting with automated lesion detection. Aim of this study was to evaluate the effect of such a software with AI supported lesion detection to the radiologic reporting.

The aim of this study was to employ artificial intelligence (AI)-based magnetic resonance imaging (MRI) brain volumetry to potentially distinguish between idiopathic normal pressure hydrocephalus (iNPH), Alzheimer’s disease (AD), and age- and sex-matched healthy controls (CG) by evaluating cortical, subcortical, and ventricular volumes.

Automatic lesion counting and volumetry is feasible with both 2D- and 3D-weightend FLAIR images. Still, it leads to partly significant differences even between two sequences that both are conform to the 2021 MAGNIMS-CMSC NAIMS consensus recommendations.