Background: The development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using artificial intelligence. However, only two papers have dealt with complex BI-RADS classifications.
View Article and Find Full Text PDFImage quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence.
View Article and Find Full Text PDFPurpose: Severe acute pancreatitis (AP) is still a significant clinical problem which is associated with a highly mortality. The aim of this study was the evaluation of prognostic value of CT regional perfusion measurement performed on the first day of onset of symptoms of AP, in assessing the risk of developing severe form of acute pancreatitis.
Material And Methods: 79 patients with clinical symptoms and biochemical criteria indicative of acute pancreatitis (acute upper abdominal pain, elevated levels of serum amylase and lipase) underwent perfusion CT within 24 hours after onset of symptoms.