Das Suchergebnis hat sich seit Ihrer Suchanfrage verändert. Eventuell werden Dokumente in anderer Reihenfolge angezeigt.
  • Treffer 4 von 5
Zurück zur Trefferliste

Effect of AI on performance of endoscopists to detect Barrett neoplasia: A Randomized Tandem Trial

  • Background and study aims To evaluate the effect of an AI-based clinical decision support system (AI) on the performance and diagnostic confidence of endoscopists during the assessment of Barrett's esophagus (BE). Patients and Methods Ninety-six standardized endoscopy videos were assessed by 22 endoscopists from 12 different centers with varying degrees of BE experience. The 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 standalone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.6%, 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 69.7% (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 the additional AI. BE experts and nonexperts remained below the standalone performance of AI, suggesting that there may be other factors influencing endoscopists to follow or discard AI advice.

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar Anzahl der Zugriffe auf dieses Dokument
Metadaten
Verfasserangaben:Michael MeinikheimORCiD, Robert MendelORCiD, Christoph PalmORCiDGND, Andreas Probst, Anna Muzalyova, Markus Wolfgang Scheppach, Sandra Nagl, Elisabeth Schnoy, Christoph RömmeleORCiD, Dominik Andreas Helmut Otto Schulz, Jakob Schlottmann, Friederike Prinz, David Rauber, Tobias RückertORCiD, Tomoaki MatsumuraORCiD, Glòria Fernández-Esparrach, Nasim Parsa, Michael F Byrne, Helmut Messmann, Alanna EbigboORCiD
DOI:https://doi.org/10.1055/a-2296-5696
ISSN:0013-726X
Pubmed-Id:https://pubmed.ncbi.nlm.nih.gov/38547927
Titel des übergeordneten Werkes (Englisch):Endoscopy
Verlag:Georg Thieme Verlag
Dokumentart:Artikel aus einer Zeitschrift/Zeitung
Sprache der Veröffentlichung:Englisch
Jahr der Fertigstellung:2024
Datum der Freischaltung:25.04.2024
Bemerkung:
Accepted Manuscript
Fakultäten / Institute / Einrichtungen:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Forschungsschwerpunkt:Lebenswissenschaften und Ethik
OpenAccess Publikationsweg:Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Lizenz (Deutsch):Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International