Publication Name	World Journal of Gastrointestinal Endoscopy
Manuscript ID	101233
Country	China

Received	2024-09-09 06:58
Peer-Review Started	2024-09-12 01:07
To Make the First Decision
Return for Revision	2024-11-11 09:55
Revised	2024-11-21 16:24
Second Decision	2024-12-06 02:42
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-12-06 08:53
Articles in Press	2024-12-06 08:53
Publication Fee Transferred	2024-11-24 02:23
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-12-20 06:57
Publish the Manuscript Online	2025-01-16 09:06

ISSN	1948-5190 (online)
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Category	Computer Science, Interdisciplinary Applications
Manuscript Type	Case Report
Article Title	Multimodal artificial intelligence system for detecting a small esophageal high-grade squamous intraepithelial neoplasia: A case report
Manuscript Source	Unsolicited Manuscript
All Author List	Yang Zhou, Rui-De Liu, Hui Gong, Xiang-Lei Yuan, Bing Hu and Zhi-Yin Huang
ORCID	Author(s) ORCID Number Yang Zhou http://orcid.org/0009-0003-8845-5885 Rui-De Liu http://orcid.org/0009-0000-7414-6326 Hui Gong http://orcid.org/0000-0001-7389-2167 Xiang-Lei Yuan http://orcid.org/0000-0003-2281-5094 Bing Hu http://orcid.org/0000-0002-9898-8656 Zhi-Yin Huang http://orcid.org/0000-0002-8322-1786
Funding Agency and Grant Number	Funding Agency Grant Number 135 High-end Talent Project of West China Hospital, Sichuan University No. ZYDG23029
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
Key Words	Artificial intelligence; Multimodal artificial intelligence system; Esophageal squamous cell carcinoma; High-grade intraepithelial neoplasia; Case report
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.
Publish Date	2025-01-16 09:06
Citation	<p>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. <i>World J Gastrointest Endosc</i> 2025; 17(1): 101233</p>
URL	https://www.wjgnet.com/1948-5190/full/v17/i1/101233.htm
DOI	https://dx.doi.org/10.4253/wjge.v17.i1.101233

Full Article (PDF)	WJGE-17-101233-with-cover.pdf
CARE Checklist–2016	101233-CARE-Checklist-2016.pdf
Manuscript File	101233_Auto_Edited_020116.docx
Answering Reviewers	101233-answering-reviewers.pdf
Audio Core Tip	101233-audio.mp3
Conflict-of-Interest Disclosure Form	101233-conflict-of-interest-statement.pdf
Copyright License Agreement	101233-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	101233-foundation-statement.pdf
Signed Consent for Treatment Form(s) or Document(s)	101233-informed-consent-statement.pdf
Non-Native Speakers of English Editing Certificate	101233-non-native-speakers.pdf
Peer-review Report	101233-peer-reviews.pdf
Scientific Misconduct Check	101233-scientific-misconduct-check.png
Scientific Editor Work List	101233-scientific-editor-work-list.pdf
CrossCheck Report	101233-crosscheck-report.pdf