Editorial
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Surg. Oct 27, 2024; 16(10): 3091-3093
Published online Oct 27, 2024. doi: 10.4240/wjgs.v16.i10.3091
Advancing perioperative optimization in Crohn's disease surgery with machine learning predictions
Olga Maria Nardone, Fabiana Castiglione, Simone Maurea
Olga Maria Nardone, Department of Public Health, University of Naples Federico II, Naples 80131, Italy
Fabiana Castiglione, Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples 80131, Italy
Simone Maurea, Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples 80131, Italy
Author contributions: Nardone OM was responsible for conceptualization, writing original draft, review, and editing; Castiglione F and Maurea S were responsible for writing, review, editing, and supervision; all authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors declare that they have no conflict of interest.
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: Olga Maria Nardone, MD, PhD, Assistant Professor, Department of Public Health, University of Naples Federico II, Via Pansini 5, Naples 80131, Italy. olgamaria.nardone@unina.it
Received: May 8, 2024
Revised: June 28, 2024
Accepted: July 9, 2024
Published online: October 27, 2024
Processing time: 141 Days and 14.2 Hours
Core Tip

Core Tip: Preoperative optimization for patients with Crohn's disease presents numerous challenges. Traditional research methods are insufficient to address the expanding array of questions in this field. Hence, there is a growing need to explore and develop new tools grounded in advanced technologies, such as computer simulation models and artificial intelligence. Employing machine learning approaches can significantly enhance patient risk stratification and the formulation of preoperative optimization strategies, ultimately aiming to improve post-surgical outcomes.