Machine‐Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study

Machine‐Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study

ABSTRACT Background Endoscopic submucosal dissection (ESD) is a minimally invasive treatment for early gastric cancer (GC); however, post‐ESD bleeding remains a serious and unpredictable complication. This study aimed to develop machine‐learning (ML) models to predict post‐ESD bleeding and identify...

Full description

Saved in:
Bibliographic Details
Main Authors: Hiroki Maruyama, Kazuya Takahashi, Kosuke Kojima, Nao Nakajima, Hiroki Sato, Ken‐ichi Mizuno, Soichi Sugitani, Shuji Terai
Format: Article
Language:English
Published: Wiley 2025-07-01
Series:JGH Open
Subjects:
artificial intelligence
bleeding
early gastric cancer
endoscopic submucosal dissection
feature importance
machine learning
Online Access:https://doi.org/10.1002/jgh3.70203
Tags: Add Tag
No Tags, Be the first to tag this record!
Be the first to leave a comment!
You must be logged in first

Similar Items