Letter to the Editor
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Aug 27, 2024; 16(8): 2745-2747
Published online Aug 27, 2024. doi: 10.4240/wjgs.v16.i8.2745
Machine learning in predicting postoperative complications in Crohn’s disease
Li-Fan Zhang, Liu-Xiang Chen, Wen-Juan Yang, Bing Hu
Li-Fan Zhang, Liu-Xiang Chen, Wen-Juan Yang, Bing Hu, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Li-Fan Zhang, Liu-Xiang Chen, Wen-Juan Yang, Bing Hu, Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Zhang LF contributed to conceptualization, writing the original draft, and manuscript review and editing; Chen LX contributed to writing the original draft, and manuscript review and editing; Yang WJ contributed to writing the original draft; Hu B contributed to writing the original draft and supervision.
Supported by the Natural Science Foundation of Sichuan Province, No. 2022NSFSC0819.
Conflict-of-interest statement: The authors declare that they have no conflict of interest to disclose.
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: Bing Hu, MD, Professor, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, China. hubing@wchscu.edu.cn
Received: March 27, 2024
Revised: July 6, 2024
Accepted: July 15, 2024
Published online: August 27, 2024
Processing time: 142 Days and 1 Hours
Abstract

Crohn's disease (CD) is a chronic inflammatory bowel disease of unknown origin that can cause significant disability and morbidity with its progression. Due to the unique nature of CD, surgery is often necessary for many patients during their lifetime, and the incidence of postoperative complications is high, which can affect the prognosis of patients. Therefore, it is essential to identify and manage postoperative complications. Machine learning (ML) has become increasingly important in the medical field, and ML-based models can be used to predict postoperative complications of intestinal resection for CD. Recently, a valuable article titled “Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study” was published by Wang et al. We appreciate the authors' creative work, and we are willing to share our views and discuss them with the authors.

Keywords: Crohn’s disease; Intestinal resection; Postoperative complications; Machine learning; Explainability

Core Tip: Crohn's disease (CD) is a condition characterized by chronic, recurrent inflammation, which can involve any part of the digestive tract. As the disease progresses, many patients require bowel resection and ostomy, with subsequent postoperative complications. Machine learning (ML) has emerged as a valuable tool for predicting these complications. A recent study published by Wang et al presented an ML approach for predicting major postoperative complications in CD patients undergoing intestinal resection. While acknowledging the merit of the study, we would like to express our opinions and engage in a discussion with the authors.