Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy

Abstract: Background and study aims Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy ; volume:10 ; number:03 ; year:2022 ; pages:E262-E268
Endoscopy International Open ; 10, Heft 03 (2022), E262-E268

Beteiligte Personen und Organisationen
Pereira, Pedro
Mascarenhas, Miguel
Ribeiro, Tiago
Afonso, João
Ferreira, João P. S.
Vilas-Boas, Filipe
Parente, Marco P.L.
Jorge, Renato N.
Macedo, Guilherme

DOI
10.1055/a-1723-3369
URN
urn:nbn:de:101:1-2022050510494961415924
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
15.08.2025, 07:25 MESZ

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Beteiligte

  • Pereira, Pedro
  • Mascarenhas, Miguel
  • Ribeiro, Tiago
  • Afonso, João
  • Ferreira, João P. S.
  • Vilas-Boas, Filipe
  • Parente, Marco P.L.
  • Jorge, Renato N.
  • Macedo, Guilherme

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