TY - JOUR A1 - Scheppach, Markus W. A1 - Rauber, David A1 - Stallhofer, Johannes A1 - Muzalyova, Anna A1 - Otten, Vera A1 - Manzeneder, Carolin A1 - Schwamberger, Tanja A1 - Wanzl, Julia A1 - Schlottmann, Jakob A1 - Tadic, Vidan A1 - Probst, Andreas A1 - Schnoy, Elisabeth A1 - Römmele, Christoph A1 - Fleischmann, Carola A1 - Meinikheim, Michael A1 - Miller, Silvia A1 - Märkl, Bruno A1 - Stallmach, Andreas A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Ebigbo, Alanna T1 - Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm JF - Gastrointestinal Endoscopy N2 - Background and aims Celiac disease with its endoscopic manifestation of villous atrophy is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of villous atrophy at routine esophagogastroduodenoscopy may improve diagnostic performance. Methods A dataset of 858 endoscopic images of 182 patients with villous atrophy and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet 18 deep learning model to detect villous atrophy. An external data set was used to test the algorithm, in addition to six fellows and four board certified gastroenterologists. Fellows could consult the AI algorithm’s result during the test. From their consultation distribution, a stratification of test images into “easy” and “difficult” was performed and used for classified performance measurement. Results External validation of the AI algorithm yielded values of 90 %, 76 %, and 84 % for sensitivity, specificity, and accuracy, respectively. Fellows scored values of 63 %, 72 % and 67 %, while the corresponding values in experts were 72 %, 69 % and 71 %, respectively. AI consultation significantly improved all trainee performance statistics. While fellows and experts showed significantly lower performance for “difficult” images, the performance of the AI algorithm was stable. Conclusion In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of villous atrophy on endoscopic still images. AI decision support significantly improved the performance of non-expert endoscopists. The stable performance on “difficult” images suggests a further positive add-on effect in challenging cases. KW - celiac disease KW - villous atrophy KW - endoscopy detection KW - artificial intelligence Y1 - 2023 U6 - https://doi.org/10.1016/j.gie.2023.01.006 PB - Elsevier ER - TY - CHAP A1 - Graf, Julian A1 - Neubauer, Katrin A1 - Fischer, Sebastian A1 - Hackenberg, Rudolf T1 - Architecture of an intelligent Intrusion Detection System for Smart Home T2 - 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops): 2020, Austin, Texas, USA N2 - Increasing cyber-attacks on Internet of Things (IoT) environments are a growing problem of digitized households worldwide. The purpose of this study is to investigate how an intelligent Intrusion Detection System (iIDS) can provide more security in IoT networks with a novel architecture, combining multiple classical and machine learning approaches. By combining classical security analysis methods and modern concepts of artificial intelligence, we increase the quality of attack detection and can therefore conduct dedicated attack suppression. The architectural image of the iIDS consists of different layers, which in parts achieve self-sufficient results. The results of the different modules are calculated by means of statement variables and evaluation techniques adapted for the individual module elements and subsequently combined by limit value considerations. The architecture image combines approaches for the analysis and processing of IoT network traffic and evaluates it to an aggregated score. From this result it can be determined whether the analyzed data indicates device misuse or attempted break-ins into the network. This study answers the questions whether a connection between classical and modern concepts for monitoring and analyzing IoT network traffic can be implemented meaningfully within a reliable architecture of an iIDS. KW - Internet of Things KW - artificial intelligence KW - smart Home KW - Intrusion KW - Detection System Y1 - 2020 SN - 978-1-7281-4716-1 U6 - https://doi.org/10.1109/PerComWorkshops48775.2020.9156168 SP - 1 EP - 6 PB - IEEE ER -