©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 21, 2024; 30(43): 4669-4671
Published online Nov 21, 2024. doi: 10.3748/wjg.v30.i43.4669
Published online Nov 21, 2024. doi: 10.3748/wjg.v30.i43.4669
Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward
Hong-Niu Wang, Jia-Hao An, Department of Graduate School of Medicine, Changzhi Medical College, Changzhi 046000, Shanxi Province, China
Liang Zong, Department of Gastrointestinal Surgery, Changzhi People’s Hospital, Changzhi 046000, Shanxi Province, China
Author contributions: Wang HN and An JH drafted the initial manuscript; Zong L reviewed the manuscript; All three authors contributed to the work.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Liang Zong, PhD, Doctor, Department of Gastrointestinal Surgery, Changzhi People’s Hospital, No. 502 Changxing Middle Road, Changzhi 046000, Shanxi Province, China. 250537471@qq.com
Received: August 3, 2024
Revised: September 25, 2024
Accepted: October 18, 2024
Published online: November 21, 2024
Processing time: 89 Days and 0.7 Hours
Revised: September 25, 2024
Accepted: October 18, 2024
Published online: November 21, 2024
Processing time: 89 Days and 0.7 Hours
Core Tip
Core Tip: Hong et al developed a predictive scoring system that uses machine learning techniques including LASSO regression, random forests, and artificial neural networks to assess complications following laparoscopic radical gastrectomy for gastric cancer. Their model, which was validated using data from multiple centers, showed high diagnostic accuracy and sensitivity, particularly with the random forest method. This innovative artificial intelligence-driven approach enhances surgical safety, reduces complication risks, and offers a valuable tool for both preoperative and postoperative decision-making, particularly for less-experienced gastroenterologists managing gastric cancer cases.