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

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

doi:10.1055/a-1723-3369

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

Endosc Int Open. 2022 Mar 14;10(3):E262-E268. doi: 10.1055/a-1723-3369. eCollection 2022 Mar.

Authors

Pedro Pereira^{1

2

3}, Miguel Mascarenhas^{1

2

3}, Tiago Ribeiro^{1

2}, João Afonso^{1

2}, João P S Ferreira^{4

5}, Filipe Vilas-Boas^{1

2

3}, Marco P L Parente^{4

5}, Renato N Jorge^{4

5}, Guilherme Macedo^{1

2

3}

Affiliations

¹ Department of Gastroenterology, São João University Hospital, Porto, Portugal.
² WGO Gastroenterology and Hepatology Training Center, Porto, Portugal.
³ Faculty of Medicine of the University of Porto, Porto, Portugal.
⁴ Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal.
⁵ INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.

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.

The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).