Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial

  • Background This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE). Methods 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett’s esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. Results AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%–74.2%] to 78.0% [95%CI 74.0%–82.0%]; specificity 67.3% [95%CI 62.5%–72.2%] to 72.7% [95%CI 68.2%–77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. Conclusion BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists’ decisions to follow or discard AI advice.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Author:Michael MeinikheimORCiD, Robert MendelORCiD, Christoph PalmORCiDGND, Andreas Probst, Anna Muzalyova, Markus W. Scheppach, Sandra Nagl, Elisabeth Schnoy, Christoph RömmeleORCiD, Dominik A. H. Schulz, Jakob Schlottmann, Friederike Prinz, David Rauber, Tobias RueckertORCiD, Tomoaki MatsumuraORCiD, Glòria Fernández-Esparrach, Nasim Parsa, Michael F. Byrne, Helmut Messmann, Alanna EbigboORCiD
Parent Title (English):Endoscopy
Publisher:Georg Thieme Verlag
Place of publication:Stuttgart
Document Type:Article
Year of first Publication:2024
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2024/05/15
Tag:Artificial Intelligence; Endoscopy; Medical Image Computing
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
OpenAccess Publikationsweg:Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Licence (German):Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International