• Treffer 4 von 8
Zurück zur Trefferliste

Endoscopic Diagnosis of Eosinophilic Esophagitis Using a deep Learning Algorithm

  • Aims Eosinophilic esophagitis (EoE) is easily missed during endoscopy, either because physicians are not familiar with its endoscopic features or the morphologic changes are too subtle. In this preliminary paper, we present the first attempt to detect EoE in endoscopic white light (WL) images using a deep learning network (EoE-AI). Methods 401 WL images of eosinophilic esophagitis and 871 WL images of normal esophageal mucosa were evaluated. All images were assessed for the Endoscopic Reference score (EREFS) (edema, rings, exudates, furrows, strictures). Images with strictures were excluded. EoE was defined as the presence of at least 15 eosinophils per high power field on biopsy. A convolutional neural network based on the ResNet architecture with several five-fold cross-validation runs was used. Adding auxiliary EREFS-classification branches to the neural network allowed the inclusion of the scores as optimization criteria during training. EoE-AI was evaluated for sensitivity, specificity, and F1-score. In addition, two human endoscopists evaluated the images. Results EoE-AI showed a mean sensitivity, specificity, and F1 of 0.759, 0.976, and 0.834 respectively, averaged over the five distinct cross-validation runs. With the EREFS-augmented architecture, a mean sensitivity, specificity, and F1-score of 0.848, 0.945, and 0.861 could be demonstrated respectively. In comparison, the two human endoscopists had an average sensitivity, specificity, and F1-score of 0.718, 0.958, and 0.793. Conclusions To the best of our knowledge, this is the first application of deep learning to endoscopic images of EoE which were also assessed after augmentation with the EREFS-score. The next step is the evaluation of EoE-AI using an external dataset. We then plan to assess the EoE-AI tool on endoscopic videos, and also in real-time. This preliminary work is encouraging regarding the ability for AI to enhance physician detection of EoE, and potentially to do a true “optical biopsy” but more work is needed.

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar Anzahl der Zugriffe auf dieses Dokument
Metadaten
Verfasserangaben:Christoph Römmele, Robert MendelORCiD, David Rauber, Tobias RückertORCiD, Michael F. Byrne, Christoph PalmORCiDGND, Helmut Messmann, Alanna EbigboORCiD
DOI:https://doi.org/10.1055/s-0041-1724274
Titel des übergeordneten Werkes (Englisch):Endoscopy
Verlag:Georg Thieme Verlag
Verlagsort:Stuttgart
Dokumentart:Konferenzveröffentlichung (Präsentation, Abstract)
Sprache der Veröffentlichung:Englisch
Jahr der Veröffentlichung:2021
Datum der Freischaltung:30.07.2021
Freies Schlagwort / Tag:Deep Learning; Endoscopy; Eosinophilic Esophagitis
Jahrgang:53
Ausgabe / Heft:S 01
Aufsatznummer:S10
Konferenzangabe:ESGE Days 2021
Fakultäten / Institute / Einrichtungen:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:peer-reviewed
Forschungsschwerpunkt:Lebenswissenschaften und Ethik
Lizenz (Deutsch):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG