An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis

  • 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.

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Author:Christoph Römmele, Robert MendelORCiD, Caroline Barrett, Hans KieslORCiDGND, David Rauber, Tobias RückertORCiD, Lisa Kraus, Jakob Heinkele, Christine Dhillon, Bianca Grosser, Friederike Prinz, Julia Wanzl, Carola Fleischmann, Sandra Nagl, Elisabeth Schnoy, Jakob Schlottmann, Evan S. Dellon, Helmut Messmann, Christoph PalmORCiDGND, Alanna EbigboORCiD
Parent Title (English):Scientific Reports
Publisher:Nature Portfolio
Place of publication:London
Document Type:Article
Year of first Publication:2022
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2022/07/07
Tag:Artificial Intelligence; Smart Endoscopy; eosinophilic esophagitis
Article Number:11115
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
research focus:Lebenswissenschaften und Ethik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International