Zhou Y, Liu RD, Gong H, Yuan XL, Hu B, Huang ZY. Multimodal artificial intelligence system for detecting a small esophageal high-grade squamous intraepithelial neoplasia: A case report. World J Gastrointest Endosc 2025; 17(1): 101233 [DOI: 10.4253/wjge.v17.i1.101233]
Corresponding Author of This Article
Zhi-Yin Huang, Associate Professor, MD, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou district, Chengdu 610041, Sichuan Province, China. huangzhiyin@wchscu.edu.cn
Research Domain of This Article
Computer Science, Interdisciplinary Applications
Article-Type of This Article
Case Report
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Yang Zhou, Rui-De Liu, Hui Gong, Xiang-Lei Yuan, Zhi-Yin Huang, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Bing Hu, Department of Gastroenterology and Hepatology, Medical Engineering Integration Laboratory of Digestive Endoscopy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Zhou Y and Liu RD contributed to manuscript writing and editing; Gong H and Yuan XL contributed to multimodal artificial intelligence system training; Hu B contributed to endoscopy examination and endoscopic submucosal dissection; Huang ZY contributed to manuscript editing; and all authors thoroughly reviewed and endorsed the final manuscript.
Supported by the 135 High-end Talent Project of West China Hospital, Sichuan University, No. ZYDG23029.
Informed consent statement: All study participants or their legal guardian provided informed written consent about personal and medical data collection prior to study enrolment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
CARE Checklist (2016) statement: The authors have read the CARE Checklist (2016), and the manuscript was prepared and revised according to the CARE Checklist (2016).
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhi-Yin Huang, Associate Professor, MD, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou district, Chengdu 610041, Sichuan Province, China. huangzhiyin@wchscu.edu.cn
Received: September 9, 2024 Revised: November 21, 2024 Accepted: December 6, 2024 Published online: January 16, 2025 Processing time: 129 Days and 20.3 Hours
Abstract
BACKGROUND
Recent advancements in artificial intelligence (AI) have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases. AI has shown great promise in clinical practice, particularly for diagnostic support, offering real-time insights into complex conditions such as esophageal squamous cell carcinoma.
CASE SUMMARY
In this study, we introduce a multimodal AI system that successfully identified and delineated a small and flat carcinoma during esophagogastroduodenoscopy, highlighting its potential for early detection of malignancies. The lesion was confirmed as high-grade squamous intraepithelial neoplasia, with pathology results supporting the AI system’s accuracy. The multimodal AI system offers an integrated solution that provides real-time, accurate diagnostic information directly within the endoscopic device interface, allowing for single-monitor use without disrupting endoscopist’s workflow.
CONCLUSION
This work underscores the transformative potential of AI to enhance endoscopic diagnosis by enabling earlier, more accurate interventions.
Core Tip: This study introduces a novel multimodal artificial intelligence system (MAIS) based on the QueryInst network for real-time detection and delineation of esophageal squamous cell carcinoma and precancerous lesions during endoscopy. Unlike traditional artificial intelligence systems, MAIS integrates directly into the endoscopic device, allowing for single-monitor use without altering the endoscopist’s workflow. This case report demonstrates its ability to accurately identify a flat esophageal lesion, which was confirmed as high-grade squamous intraepithelial neoplasia. The findings highlight potential of MAIS for improving early diagnosis and biopsy accuracy in high-risk gastrointestinal conditions such as esophageal squamous cell carcinoma.
