Regensburg Center of Health Sciences and Technology - RCHST
Refine
Year of publication
- 2022 (2) (remove)
Document Type
- Article (1)
- Working Paper (1)
Has Fulltext
- yes (2) (remove)
Is part of the Bibliography
- no (2)
Keywords
Institute
- Fakultät Informatik und Mathematik (2) (remove)
Begutachtungsstatus
- peer-reviewed (1)
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-EoEEREFS) 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.
Das Forschungsprojekt „Dein Haus 4.0 Oberpfalz – Telepräsenzroboter für die Pflege und Unterstützung von Schlaganfallpatientinnen und -patienten (TePUS)“ untersucht den Einsatz von zwei Varianten von Telepräsenzrobotern. Das Arbeitspapier stellt die sozialwissenschaftliche Begleitforschung des Projekts vor. Diese hat zum einen zum Ziel, Akzeptanz und Potenzial der eingesetzten technischen Assistenzsysteme empirisch zu untersuchen. Daneben werden ethisch, rechtlich, gesellschaftlich und organisatorisch relevante Fragestellungen des Technikeinsatzes und der Mensch-Technik-Interaktion analysiert (ELSI-Begleitstudie).