Challenges in Implementing Endoscopic Artificial Intelligence: The Impact of Real-World Imaging Conditions on Barrett's Neoplasia Detection.

ABSTRACT:

Background

Endoscopic deep learning systems are often developed using high-quality imagery obtained from expert centers. Therefore, they may underperform in community hospitals where image quality is more heterogeneous.

Objective

This study aimed to quantify the performance degradation of a computer aided detection system for Barrett's neoplasia, trained on expert images, when exposed to more heterogeneous imaging conditions representative of daily clinical practice. Further, we evaluated strategies to mitigate this performance loss.

Methods

We developed a computer aided detection system using 1011 high-quality, expert-acquired images from 373 Barrett's patients. We assessed its performance on high, moderate and low image quality test sets, each containing images from

SUBMITTER: Jong MR 

PROVIDER: S-EPMC12269737 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature