Publication Name	World Journal of Gastrointestinal Endoscopy
Manuscript ID	108307
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Observational Study
Article Title	Construction and validation of a machine learning algorithm-based predictive model for difficult colonoscopy insertion
Manuscript Source	Unsolicited Manuscript
All Author List	Ren-Xuan Gao, Xin-Lei Wang, Ming-Jie Tian, Xiao-Ming Li, Jia-Jia Zhang, Jun-Jing Wang, Jing Gao, Chao Zhang and Zhi-Ting Li
Funding Agency and Grant Number
Corresponding Author	Chao Zhang, Associate Professor, Chief Physician, School of Clinical Medicine, North China University of Science and Technology, Construction South Road, Tangshan 063000, Hebei Province, China. handsomechao2025@126.com
Key Words	Colonoscopy; Difficulty of colonoscopy insertion; Machine learning algorithms; Predictive model; Logistic regression; Least absolute shrinkage and selection operator regression; Random forest
Core Tip	This study developed machine learning models to predict difficulty of colonoscopy insertion using abdominal circumference, constipation, anxiety, and clinical history. Among 712 patients, the random forest model achieved optimal performance, demonstrating high sensitivity and clinical utility. It uniquely integrates anatomical, psychological, and medical factors, offering a novel preoperative risk-stratification tool to enhance procedural success and patient comfort. This approach supports tailored interventions, improving colonoscopy quality through personalized risk assessment.
Citation	Gao RX, Wang XL, Tian MJ, Li XM, Zhang JJ, Wang JJ, Gao J, Zhang C, Li ZT. Construction and validation of a machine learning algorithm-based predictive model for difficult colonoscopy insertion. World J Gastrointest Endosc 2025; In press

Received	2025-04-11 07:23
Peer-Review Started	2025-04-11 07:23
To Make the First Decision
Return for Revision	2025-04-18 12:25
Revised	2025-05-07 05:24
Second Decision	2025-05-30 02:44
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-05-30 09:31
Articles in Press	2025-05-30 09:31
Publication Fee Transferred	2025-05-10 13:16
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-5190 (online)
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/
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