Research in the German National Educational Panel Study intends to provide the empirical basis for a longitudinal analysis of individuals' educational careers and competencies and how they unfold over the life course in relation to formal as well as nonformal/informal learning environments. In order to track educational developments and decisions over the life span, six main samples serve as the foundation for the characterization and analysis of educational processes. These six main samples include newborns, Kindergarten children, secondary school children (fifth and ninth grade), first-year undergraduate students and adults. They are accompanied Z Erziehungswiss (2011) 14:51-65 52 C. Aßmann et al.by several additional samples allowing an analysis of special groups, e.g., special needs pupils. Given that for several of the starting cohorts access to the target population is gained via educational institutions like Kindergartens and schools, multistage sampling approaches reflecting the multistage access to the target populations are implemented. Samples in individual contexts as for the cohorts of adults and newborns are established via register-based stratified cluster approaches. The designs of the implemented sampling strategies are shortly reviewed for each established sample.

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The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.