Yu HH, Chan IN, Wang JH, Qin YY, Chan IW, Wong PK. Artificial intelligence for endoscopic correlates of Correa’s cascade in gastric precancerous lesions and early neoplasia. World J Gastrointest Oncol 2026; 18(10): 123447 [DOI: 10.4251/wjgo.123447]
Corresponding Author of This Article
Pak Kin Wong, PhD, Professor, Department of Biomedical Engineering, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China. fstpkw@um.edu.mo
Research Domain of This Article
Gastroenterology & Hepatology
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review-article
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Yu HH, Chan IN, Wang JH, Qin YY, Chan IW, Wong PK. Artificial intelligence for endoscopic correlates of Correa’s cascade in gastric precancerous lesions and early neoplasia. World J Gastrointest Oncol 2026; 18(10): 123447 [DOI: 10.4251/wjgo.123447]
World J Gastrointest Oncol. Oct 15, 2026; 18(10): 123447 Published online Oct 15, 2026. doi: 10.4251/wjgo.123447
Artificial intelligence for endoscopic correlates of Correa’s cascade in gastric precancerous lesions and early neoplasia
Hon Ho Yu, In Neng Chan, Jin-Hui Wang, Ye-Ying Qin, In Weng Chan, Pak Kin Wong
Hon Ho Yu, Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
In Neng Chan, Pak Kin Wong, Department of Biomedical Engineering, University of Macau, Macau 999078, China
Jin-Hui Wang, Ye-Ying Qin, Pak Kin Wong, Department of Electromechanical Engineering, University of Macau, Macau 999078, China
In Weng Chan, Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
Co-first authors: Hon Ho Yu and In Neng Chan.
Author contributions: Yu HH and Chan IN contributed to collecting and critically reviewing the relevant literature, developing the scope and structure of the manuscript, drafting the initial manuscript, designing the figures and tables, synthesizing the key findings, and revising the manuscript, they contributed equally to this article, they are the co-first authors of this manuscript; Wang JH, Qin YY, and Chan IW conducted the investigation and contributed to writing, review, and editing; Wong PK provided supervision, project administration, and funding acquisition; and all authors read and approved the final version of the manuscript.
AI contribution statement: ChatGPT 5.2 and ChatGPT 5.5 developed by OpenAI, were used solely for language polishing, grammar refinement, formatting assistance, and improvement of manuscript clarity.
Supported by the Science and Technology Development Fund of Macau, No. 0026/2022/A.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Pak Kin Wong, PhD, Professor, Department of Biomedical Engineering, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China. fstpkw@um.edu.mo
Received: May 19, 2026 Revised: July 21, 2026 Accepted: August 14, 2026 Published online: October 15, 2026 Processing time: 123 Days and 0 Hours
Abstract
Early detection is a major determinant of survival in patients with gastric cancer. However, subtle mucosal abnormalities may be missed under white-light imaging, with detection performance influenced by endoscopist experience and examination conditions. Although deep-learning methods have driven rapid advances in artificial intelligence (AI) research for upper endoscopy, much of the available evidence remains image-based and is not consistently anchored to clinical decision points along Correa’s cascade, within which adjacent stages may coexist and reference standards vary. This review synthesizes AI applications for the detection, characterization, and extent assessment of visible endoscopic correlates of non-atrophic gastritis, multifocal atrophic gastritis, intestinal metaplasia, dysplasia, and early gastric cancer using a cascade-aware framework. Although reported accuracy is often highest in curated datasets, translation into clinical practice requires decision-aligned outputs that are robust to label noise, heterogeneous reference standards, and continuous-video artifacts. Future progress depends on prospective, multicenter, video-based validation with patient-level and procedure-level analyses. It also requires standardized reporting of operating points, workflow impact, false-positive burden per procedure, and real-time feasibility, together with studies designed to determine whether AI -assisted endoscopy improves patient outcomes beyond surrogate performance metrics.
Core Tip: This article applies Correa’s cascade as a clinical framework for evaluating artificial intelligence (AI) in upper endoscopy. Rather than directly identifying biological progression, current AI systems recognize visible endoscopic phenotypes associated with gastritis, atrophy, intestinal metaplasia, dysplasia, and early gastric cancer. By integrating histopathology, endoscopic appearance, AI task design, reference standards, validation level, and clinical decision impact, this review demonstrates why high image-level accuracy may overestimate clinical utility. Future research should emphasize prospective, multicenter, video-based validation together with patient-level and procedure-level outcome assessment.
