| 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/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| 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 |
| ORCID |
|
| Funding Agency and Grant Number |
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| 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. |
| Publish Date |
2025-07-09 07:00 |
| Citation |
<p>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. <i>World J Gastrointest Endosc</i> 2025; 17(7): 108307</p> |
| URL |
https://www.wjgnet.com/1948-5190/full/v17/i7/108307.htm |
| DOI |
https://dx.doi.org/10.4253/wjge.v17.i7.108307 |
