Prospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 7, 2025; 31(13): 104370
Published online Apr 7, 2025. doi: 10.3748/wjg.v31.i13.104370
Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm
Tian Ma, Guan-Qun Liu, Jing Guo, Rui Ji, Xue-Jun Shao, Yan-Qing Li, Zhen Li, Xiu-Li Zuo
Tian Ma, Guan-Qun Liu, Jing Guo, Rui Ji, Yan-Qing Li, Zhen Li, Xiu-Li Zuo, Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan 250012, Shandong Province, China
Xue-Jun Shao, Qingdao Medicon Digital Engineering Company Limited, Qingdao 266000, Shandong Province, China
Co-first authors: Tian Ma and Guan-Qun Liu.
Co-corresponding authors: Zhen Li and Xiu-Li Zuo.
Author contributions: Ma T and Liu GQ participated in the conceptualization, data curation, formal analysis, investigation, methodology, and writing of the original draft; Guo J and Rui J participated in investigation; Shao XJ participated in methodology; Li YQ, Li Z and Zuo XL participated in the conceptualization, supervision of the study, and editing of the manuscript; All authors contributed to the article and approved the submitted version.
Supported by the National Key Research and Development Program of China, No. 2023YFC2413800; the Taishan Scholars Program of Shandong Province, No. tsqn202306344; and the National Natural Science Foundation of China, No. 82270580 and No. 82070552.
Institutional review board statement: This study was approved by the Ethics Committee of Qilu Hospital of Shandong University (No. 2018129).
Clinical trial registration statement: The clinical trial was registered at https://clinicaltrials.gov/ (No. NCT04136236).
Informed consent statement: All study participants provided written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: There is no additional data available.
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: Xiu-Li Zuo, MD, Doctor, Department of Gastroenterology, Qilu Hospital of Shandong University, No. 107 Wenhuaxi Road, Jinan 250012, Shandong Province, China. zuoxiuli@sdu.edu.cn
Received: December 24, 2024
Revised: February 24, 2025
Accepted: March 10, 2025
Published online: April 7, 2025
Processing time: 103 Days and 5.2 Hours
Abstract
BACKGROUND

Early detection of esophageal squamous neoplasms (ESN) is essential for improving patient prognosis. Optical diagnosis of ESN remains challenging. Probe-based confocal laser endomicroscopy (pCLE) enables accurate in vivo histological observation and optical biopsy of ESN. However, interpretation of pCLE images requires histopathological expertise and extensive training. Artificial intelligence (AI) has been widely applied in digestive endoscopy; however, AI for pCLE diagnosis of ESN has not been reported.

AIM

To develop a pCLE computer-aided diagnostic system for ESN and assess its diagnostic performance and assistant efficiency for nonexpert endoscopists.

METHODS

The intelligent confocal laser endomicroscopy (iCLE) system consists of image recognition (based on inception-ResNet V2), video diagnosis, and quality judgment modules. This system was developed using pCLE images and videos and evaluated through image and prospective video recognition tests. Patients between June 2020 and January 2023 were prospectively enrolled. Expert and non-expert endoscopists and the iCLE independently performed diagnoses for pCLE videos, with histopathology as the gold standard. Thereafter, the non-expert endoscopists performed a second assessment with iCLE assistance.

RESULTS

A total of 25056 images from 2803 patients were selected for iCLE training and validation. Another 2442 images from 226 patients were used for testing. iCLE achieved a high accuracy of 98.3%, sensitivity of 95.3% and specificity of 98.8% for diagnosing ESN images. A total of 2581 patients underwent upper gastrointestinal pCLE examination and were prospectively screened; 54 patients with suspected ESN were enrolled. Overall, 187 videos from 67 lesions were assessed by iCLE, three nonexpert and three expert endoscopists. iCLE achieved a high accuracy, sensitivity and specificity of 90.9%, 92.0%, and 90.2%, respectively. Compared to experts, iCLE showed significantly higher sensitivity (92.0% vs 80.4%; P < 0.001) and negative predictive value (94.4% vs 87.7%; P = 0.003). With iCLE assistance, nonexpert endoscopists showed significant improvements in accuracy (from 83.6% to 88.6%) and sensitivity (from 76.0% to 89.8%).

CONCLUSION

iCLE system demonstrated high diagnostic performance for ESN. It can assist nonexpert endoscopists in improving the diagnostic efficiency of pCLE for ESN and has the potential for reducing unnecessary biopsies.

Keywords: Esophageal squamous neoplasm; Probe-based confocal laser endomicroscopy; Optical biopsy; Artificial intelligence; Computer aided diagnosis

Core Tip: The optical diagnosis of esophageal squamous neoplasms (ESN) is challenging. Probe-based confocal laser endomicroscopy (pCLE) enables the optical biopsy of ESN; however, its application is affected by the difficulty of image interpretation. This study developed the first pCLE computer-aided diagnostic system (intelligent confocal laser endomicroscopy) for ESN, enabling real-time diagnosis of pCLE videos. Intelligent confocal laser endomicroscopy was evaluated in a prospective study and compared with endoscopists with different expertise levels. It demonstrated higher sensitivity than both nonexpert and expert endoscopists. Additionally, it assists nonexperts in significantly improving diagnostic accuracy and sensitivity. This system has the potential to assist endoscopists in the application of pCLE and reduce unnecessary biopsies.