Application of artificial intelligence-assisted confocal laser endomicroscopy in gastrointestinal imaging analysis
Year: 2025
Type: article
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.
Authors: Yu-Shun Liu, Ze-Hua Shi, Yan-Rui Jin, Cui-Ping Yang, Cheng-Liang Liu
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Awards: G1299923577, G4934851719, G9000241293
OpenAlex ID: W4411048089