Vendor-Agnostic Vision Transformer-Based Artificial Intelligence for Peroral Cholangioscopy: Diagnostic Performance in Biliary Strictures Compared With Convolutional Neural Networks and Endoscopists.

ABSTRACT:

Objectives

Accurate diagnosis of biliary strictures remains challenging. This study aimed to develop an artificial intelligence (AI) system for peroral cholangioscopy (POCS) using a Vision Transformer (ViT) architecture and to evaluate its performance compared to different vendor devices, conventional convolutional neural networks (CNNs), and endoscopists.

Methods

We retrospectively analyzed 125 patients with indeterminate biliary strictures who underwent POCS between 2012 and 2024. AI models including the ViT architecture and two established CNN architectures were developed using images from CHF-B260 or B290 (CHF group; Olympus Medical) and SpyScope DS or DS II (Spy group; Boston Scientific) systems via a patient-level, 3-fold cross-validation. For a direct comparison again

SUBMITTER: Sato R 

PROVIDER: S-EPMC12702826 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature