Performance comparison of a deep learning algorithm with endoscopists in the detection of duodenal villous atrophy (VA)

  • Aims  VA is an endoscopic finding of celiac disease (CD), which can easily be missed if pretest probability is low. In this study, we aimed to develop an artificial intelligence (AI) algorithm for the detection of villous atrophy on endoscopic images. Methods 858 images from 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa were used for training and internal validation of an AI algorithm (ResNet18). A separate dataset was used for external validation, as well as determination of detection performance of experts, trainees and trainees with AI support. According to the AI consultation distribution, images were stratified into “easy” and “difficult”. Results Internal validation showed 82%, 85% and 84% for sensitivity, specificity and accuracy. External validation showed 90%, 76% and 84%. The algorithm was significantly more sensitive and accurate than trainees, trainees with AI support and experts in endoscopy. AI support in trainees was associated with significantly improved performance. While all endoscopists showed significantly lower detection for “difficult” images, AI performance remained stable. Conclusions The algorithm outperformed trainees and experts in sensitivity and accuracy for VA detection. The significant improvement with AI support suggests a potential clinical benefit. Stable performance of the algorithm in “easy” and “difficult” test images may indicate an advantage in macroscopically challenging cases.

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Author:Markus ScheppachORCiD, David Rauber, Johannes Stallhofer, Anna Muzalyova, Vera Otten, Carolin Manzeneder, Tanja Schwamberger, Julia Wanzl, Jakob Schlottmann, Vidan Tadic, Andreas Probst, Elisabeth Schnoy, Christoph Römmele, Carola Fleischmann, Michael Meinikheim, Silvia Miller, Bruno Märkl, Christoph PalmORCiDGND, Helmut Messmann, Alanna EbigboORCiD
Parent Title (English):Endoscopy
Document Type:conference proceeding (presentation, abstract)
Year of first Publication:2023
Release Date:2023/05/08
Konferenzangabe:ESGE Days 2023
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG