A Novel Switching of Artificial Intelligence to Generate Simultaneously Multimodal Images to Assess Inflammation and Predict Outcomes in Ulcerative Colitis-(With Video).

ABSTRACT:

Objectives

Virtual Chromoendoscopy (VCE) is pivotal for assessing activity and predicting outcomes in Ulcerative Colitis (UC), though interobserver variability and the need for expertise persist. Artificial intelligence (AI) offers standardized VCE-based assessment. This study introduces a novel AI model to detect and simultaneously generate various endoscopic modalities, enhancing AI-driven inflammation assessment and outcome prediction in UC.

Methods

Endoscopic videos in high-definition white-light, iScan2, iScan3, and NBI from UC patients of the international PICaSSO iScan and NBI cohort (302 and 54 patients, respectively) were used to develop a neural network to identify the acquisition modality of each frame and for inter-modality image switching. 2535 frames from 169 v

SUBMITTER: Iacucci M 

PROVIDER: S-EPMC12511918 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature