Automatic Identification of Papillary Projections in Indeterminate Biliary Strictures Using Digital Single-Operator Cholangioscopy

Abstract

Introduction: Characterization of biliary strictures is challenging. Papillary projections (PP) are often reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy. 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 PP in digital single-operator cholangioscopy images.

Methods: A convolutional neural network (CNN) was developed. Each frame was evaluated for the presence of PP. The CNN's performance was measured by the area under the curve, sensitivity, specificity, and positive and negative predictive values.

Results: A total of 3,920 images from 85 patients were included. Our model had a sensitivity and specificity 99.7% and 97.1%, respectively. The area under the curve was 1.00.

Discussion: Our CNN was able to detect PP with high accuracy. Future development of AI tools may optimize the macroscopic characterization of biliary strictures.

Conflict of interest statement

Guarantor of the article: Tiago Ribeiro, MD, MSc.

Specific author contributions: T.R. and M.M.S.—study design, revision of D-SOC videos, image extraction and labeling and construction and development of the CNN, and data interpretation and drafting of the manuscript. J.A.—study design, revision of D-SOC videos, construction, and development of the CNN. J.P.S.F.—study design, construction and development of the CNN, and statistical analysis. P.P. and F.V.B.S.—equal contribution in study design, construction and development of the CNN, and data interpretation and drafting of the manuscript. M.P.L.P, R.N.J., and G.M.—study design and revision of the scientific content of the manuscript. All authors approved the final version of this manuscript.

Financial support: The authors acknowledge Fundação para a Ciência e Tecnologia (FCT) for supporting the computational costs related to this study through CPCA/A0/7363/2020 grant. This entity had no role in study design, data collection, data analysis, preparation of the manuscript, and publishing decision.

Potential competing interests: None to report.

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.

Figures

Graphical abstract

Figure 1.

Output obtained during the training and development of the convolutional neural network. The bars represent the probability estimated by the network. The finding with the highest probability was outputted as the predicted classification. A blue bar represents a correct prediction. Red bars represent an incorrect prediction. B, benign biliary findings; PP, papillary projections.

Figure 2.

ROC analysis of the network's performance in the detection of malignant biliary strictures or benign biliary conditions. ROC, receiver operating characteristic; PP, papillary projections.