Minireviews
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 21, 2025; 31(23): 105076
Published online Jun 21, 2025. doi: 10.3748/wjg.v31.i23.105076
Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders
Shao-Wen Liu, Peng Li, Xiao-Qing Li, Qi Wang, Jin-Yu Duan, Jin Chen, Ru-Hong Li, Yang-Fan Guo
Shao-Wen Liu, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650031, Yunnan Province, China
Peng Li, Ru-Hong Li, Department of General Surgery II, Yan’an Hospital Affiliated to Kunming Medical University, Kunming 650051, Yunnan Province, China
Xiao-Qing Li, Yang-Fan Guo, Precision Medicine Center, Yan’an Hospital Affiliated to Kunming Medical University, Kunming 650051, Yunnan Province, China
Xiao-Qing Li, Qi Wang, Yang-Fan Guo, Central Laboratory, Yan’an Hospital Affiliated to Kunming Medical University, Kunming 650051, Yunnan Province, China
Xiao-Qing Li, Yang-Fan Guo, Yunnan Key Laboratory of Tumor Immunological Prevention and Control, Yan’an Hospital Affiliated to Kunming Medical University, Kunming 650051, Yunnan Province, China
Jin-Yu Duan, Department of Information, The Third People’s Hospital of Kunming, Kunming 650041, Yunnan Province, China
Jin Chen, Department of Information, The Third People’s Hospital of Yunnan Province, Kunming 650041, Yunnan Province, China
Co-first authors: Shao-Wen Liu and Peng Li.
Author contributions: Guo YF conceived the study; Liu SW, Li P, Wang Q, Duan JY, and Chen J reviewed the literature and analyzed the data; Liu SW and Guo YF drafted the manuscript; Li XQ created the figure; Li RH and Guo YF revised the manuscript; All authors have read and approved the manuscript; Liu SW and Li P contributed equally to this work.
Supported by the Central Funds Guiding the Local Science and Technology Development, No. 202207AB110017; Key Research and Development Program of Yunnan, No. 202302AD080004; Yunnan Academician and Expert Workstation, No. 202205AF150023; and the Scientific and Technological Innovation Team in Kunming Medical University, No. CXTD202215.
Conflict-of-interest statement: 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: Yang-Fan Guo, Associate Professor, Precision Medicine Center, Yan’an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, Yunnan Province, China. guoyangfan@kmmu.edu.cn
Received: January 22, 2025
Revised: May 3, 2025
Accepted: June 5, 2025
Published online: June 21, 2025
Processing time: 150 Days and 2.8 Hours
Core Tip

Core Tip: This review synthesizes machine learning (ML) applications in esophageal disorders, emphasizing three critical advances: (1) Automated analysis of multimodal diagnostic data achieving accuracy rates of 80%-95% across different conditions; (2) Integration of deep learning with endoscopic imaging enabling real-time assistance in diagnosis and risk stratification; and (3) Development of novel non-invasive screening approaches through ML-based biomarker identification. The convergence of artificial intelligence with clinical medicine demonstrates transformative potential in addressing current diagnostic challenges and enabling precision medicine in esophageal disease management.