FDA-approved deep learning software application versus radiologists with different levels of expertise: detection of intracranial hemorrhage in a retrospective single-center study.

Purpose: To assess an FDA-approved and CE-certified deep learning (DL) software application compared to the performance of human radiologists in detecting intracranial hemorrhages (ICH).

Methods: Within a 20-week trial from January to May 2020, 2210 adult non-contrast head CT scans were performed in a single center and automatically analyzed by an artificial intelligence (AI) solution with workflow integration. After excluding 22 scans due to severe motion artifacts, images were retrospectively assessed for the presence of ICHs by a second-year resident and a certified radiologist under simulated time pressure.

The "central vein sign": is there a place for susceptibility weighted imaging in possible multiple sclerosis?

Objectives: Susceptibility weighted imaging (SWI) may have the potential to depict the perivenous extent of white matter lesions (WMLs) in multiple sclerosis (MS). We aimed to assess the discriminatory value of the "central vein sign" (CVS).

Methods: In a 3-T magnetic resonance imaging (MRI) study, 28 WMLs in 14 patients with at least one circumscribed lesion >5 mm and not more than eight non-confluent lesions >3 mm were prospectively included.