Liu YS, Shi ZH, Jin YR, Yang CP, Liu CL. Application of artificial intelligence-assisted confocal laser endomicroscopy in gastrointestinal imaging analysis. Artif Intell Med Imaging 2025; 6(1): 106928 [DOI: 10.35711/aimi.v6.i1.106928]
Corresponding Author of This Article
Cui-Ping Yang, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China. yangcuipingsgh@163.com
Research Domain of This Article
Computer Science, Artificial Intelligence
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Med Imaging. Jun 8, 2025; 6(1): 106928 Published online Jun 8, 2025. doi: 10.35711/aimi.v6.i1.106928
Application of artificial intelligence-assisted confocal laser endomicroscopy in gastrointestinal imaging analysis
Yu-Shun Liu, Ze-Hua Shi, Yan-Rui Jin, Cui-Ping Yang, Cheng-Liang Liu
Yu-Shun Liu, Ze-Hua Shi, Yan-Rui Jin, Cheng-Liang Liu, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Cui-Ping Yang, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, China
Co-first authors: Yu-Shun Liu and Ze-Hua Shi.
Co-corresponding authors: Cui-Ping Yang and Cheng-Liang Liu.
Author contributions: Liu YS was responsible for the literature search and drafting the initial manuscript; Shi ZH contributed to the drafting of the manuscript and made significant revisions; Jin YR was involved in the conceptualization and revision of the manuscript; Liu YS and Shi ZH contributed equally to this work as co-first authors; Yang CP and Liu CL both jointly contributed to the overall framework design of this manuscript, clarified the writing direction; Liu CL provided important feedback and guidance throughout the writing process; Yang CP carefully reviewed the manuscript drafts in detail; Yang CP and Liu CL are recognized as co-corresponding authors.
Supported by Supported by Interdisciplinary Program of Shanghai Jiao Tong University, No. YG2024 LC01; and National Natural Science Foundation of China, No. 62406190.
Conflict-of-interest statement: All authors declare no competing interests.
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: Cui-Ping Yang, Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, No. 197 Ruijin Er Road, Shanghai 200025, China. yangcuipingsgh@163.com
Received: March 24, 2025 Revised: April 8, 2025 Accepted: April 27, 2025 Published online: June 8, 2025 Processing time: 76 Days and 2.4 Hours
Abstract
Confocal laser endomicroscopy (CLE) has become an indispensable tool in the diagnosis and detection of gastrointestinal (GI) diseases due to its high-resolution and high-contrast imaging capabilities. However, the early-stage imaging changes of gastrointestinal disorders are often subtle, and traditional medical image analysis methods rely heavily on manual interpretation, which is time-consuming, subject to observer variability, and inefficient for accurate lesion identification across large-scale image datasets. With the introduction of artificial intelligence (AI) technologies, AI-driven CLE image analysis systems can automatically extract pathological features and have demonstrated significant clinical value in lesion recognition, classification diagnosis, and malignancy prediction of GI diseases. These systems greatly enhance diagnostic efficiency and early detection capabilities. This review summarizes the applications of AI-assisted CLE in GI diseases, analyzes the limitations of current technologies, and explores future research directions. It is expected that the deep integration of AI and confocal imaging technologies will provide strong support for precision diagnosis and personalized treatment in the field of gastrointestinal disorders.
Core Tip: Confocal laser endomicroscopy (CLE) offers real-time imaging with cellular-level resolution and plays an important role in the early diagnosis of gastrointestinal (GI) diseases. However, its full clinical potential is often hindered by the subjectivity of manual interpretation and limitations in diagnostic efficiency. The rapid advancements in deep learning for medical image analysis have enabled the detection of subtle imaging changes that are easily missed by traditional methods. Through multidimensional feature analysis, these technologies can intelligently predict lesion malignancy, providing objective and efficient support for clinical decision-making. This review systematically presents the practical applications of artificial intelligence assisted CLE in the detection and diagnosis of GI diseases and discusses its future prospects in advancing precision medicine.
