Advances in deep learning for endoscopic image-based diagnosis of early gastric cancer
10.3969/j.issn.1006-5725.2025.14.006
- VernacularTitle:深度学习在早期胃癌内镜图像诊断中的研究进展
- Author:
Qian ZHANG
1
;
Yuntai CAO
1
;
Zhijie WANG
1
;
Boqi ZHOU
1
Author Information
1. 青海大学附属医院医学影像中心(青海西宁 810000)
- Publication Type:Journal Article
- Keywords:
early gastric cancer;
deep learning;
endoscopic images
- From:
The Journal of Practical Medicine
2025;41(14):2160-2166
- CountryChina
- Language:Chinese
-
Abstract:
Gastric carcinoma(GC),a highly prevalent malignant tumor globally,often progresses to advanced stages by the time of diagnosis due to its insidious clinical presentation,thereby significantly reducing therapeutic effectiveness and patient quality of life.Accurate screening and histopathological characterization of early gastric cancer(EGC)are essential for developing individualized treatment approaches.Although endoscopic techniques remain the gold standard for early GC detection,their diagnostic accuracy is largely dependent on the operator's skill,a challenge that current artificial intelligence(AI)-assisted innovations aim to address by stan-dardizing diagnostic procedures.Deep learning(DL)-based computer vision systems have demonstrated remarkable performance in identifying subtle EGC features,not only improving lesion detection sensitivity but also enabling automated assessment of key pathological indicators.These technological advances offer objective,visualized diag-nostic support for clinical decision-making.This review provides a systematic overview of recent developments in DL applications for endoscopic image analysis of EGC and evaluates their potential for clinical integration.
