Development and validation of an AI foundation model for endoscopic diagnosis of esophagogastric junction adenocarcinoma: a cohort and deep learning study.

ABSTRACT:

Background

The early detection of esophagogastric junction adenocarcinoma (EGJA) is crucial for improving patient prognosis, yet its current diagnosis is highly operator-dependent. This paper aims to make the first attempt to develop an artificial intelligence (AI) foundation model-based method for both screening and staging diagnosis of EGJA using endoscopic images.

Methods

In this cohort and learning study, we conducted a multicentre study across seven Chinese hospitals between December 28, 2016 and December 30, 2024. It comprises 12,302 images from 1546 patients (590 with advanced EGJA, 243 with early EGJA, 713 without EGJA); 8249 of them were employed for model training, while the remaining were divided into the held-out (112 patients, 914 images), external (230 patients

SUBMITTER: Ma Y 

PROVIDER: S-EPMC12510009 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature