Publications by authors named "Lena Trappmann"
Article Synopsis
- - The study investigates how microglial activation, which is linked to neuroinflammation in Alzheimer's disease (AD), relates to the connectivity of brain regions in Aβ-positive early AD patients compared to healthy controls.
- - Utilizing advanced imaging techniques like positron emission tomography (PET) and magnetic resonance imaging (MRI), researchers found that microglial activation was more prominent in functionally connected brain areas in AD patients, correlating with cognitive decline.
- - The findings suggest that, similar to tau pathology, microglial activation spreads along highly interconnected brain pathways, highlighting its potential role in the progression of neurodegeneration in AD.
Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S]CXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets of the same overall dataset, external validation is mandatory to reproduce results and reveal potential training errors. We applied a multicohort benchmarking to the publicly accessible (S)CXR analyzing AI algorithm CheXNet, comprising three clinically relevant study cohorts which differ in patient positioning ([S]CXRs), the applied reference standards (CT-/[S]CXR-based) and the possibility to also compare algorithm classification with different medical experts' reading performance.
View Article and Find Full Text PDFPneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training.
Eur Radiol
October 2021
Objectives: Diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXR) is limited by the noisy annotation quality of public training data and confounding thoracic tubes (TT). We hypothesize that in-image annotations of the dehiscent visceral pleura for algorithm training boosts algorithm's performance and suppresses confounders.
Methods: Our single-center evaluation cohort of 3062 supine CXRs includes 760 PTX-positive cases with radiological annotations of PTX size and inserted TTs.
Objectives: We hypothesized that published performances of algorithms for artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXRs) do not sufficiently consider the influence of PTX size and confounding effects caused by thoracic tubes (TTs). Therefore, we established a radiologically annotated benchmarking cohort (n = 6446) allowing for a detailed subgroup analysis.
Materials And Methods: We retrospectively identified 6434 supine CXRs, among them 1652 PTX-positive cases and 4782 PTX-negative cases.