Artificial intelligence-assisted endoscopy in diagnosing early gastric cancer: a review of research progress and clinical applications

doi:10.3877/cma.j.issn.1674-0785.2025.12.008

Abstract

Abstract:

Early diagnosis of gastric cancer is crucial for improving patient survival rates and prognosis. However, traditional endoscopic diagnosis is often hindered by the endoscopist's experience and visual recognition ability, leading to suboptimal detection rates. In recent years, artificial intelligence (AI) technology based on deep learning convolutional neural networks (CNNs) has made significant progress in endoscopic image recognition, greatly enhancing both diagnostic accuracy and efficiency. This article aims to review the current status and future clinical prospects of AI-assisted endoscopy in diagnosing early gastric cancer.

Key words: Artificial intelligence, Endoscopic diagnosis, Early gastric cancer, Deep learning, Convolutional neural networks

Tongxian Xie, Rui Zhang. Artificial intelligence-assisted endoscopy in diagnosing early gastric cancer: a review of research progress and clinical applications[J]. Chinese Journal of Clinicians(Electronic Edition), 2025, 19(12): 935-939.

/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://zhlcyszz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1674-0785.2025.12.008

https://zhlcyszz.cma-cmc.com.cn/EN/Y2025/V19/I12/935

References 50

1	Sundar R, Nakayama I, Markar SR, et al. Gastric cancer [J]. Lancet, 2025, 405(10494): 2087-2102.
2	赫捷, 陈万青, 李兆申, 等. 中国胃癌筛查与早诊早治指南(2022, 北京) [J]. 中国肿瘤, 2022, 31(7): 488-527.
3	Sharma P, Hassan C. Artificial intelligence and deep learning for upper gastrointestinal neoplasia [J]. Gastroenterology, 2022, 162(4): 1056-1066.
4	Tang D, Wang L, Ling T, et al. Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: a multicentre retrospective diagnostic study [J]. EBioMedicine, 2020, 62: 103146.
5	Dong Z, Tao X, Du H, et al. Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions [J]. J Gastroenterol, 2023, 58(10): 978-989.
6	Ebigbo A, Messmann H, Lee SH. Artificial intelligence applications in image-based diagnosis of early esophageal and gastric neoplasms [J]. Gastroenterology, 2025, 169(3): 396-415.e2.
7	Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology [J]. Semin Cancer Biol, 2023, 93: 83-96.
8	Xu Y, Tan Y, Wang Y, et al. A gratifying step forward for the application of artificial intelligence in the field of endoscopy: a narrative review [J]. Surg Laparosc Endosc Percutan Tech, 2020, 31(2): 254-263.
9	Fu XY, Mao XL, Chen YH, et al. The feasibility of applying artificial intelligence to gastrointestinal endoscopy to improve the detection rate of early gastric cancer screening [J]. Front Med (Lausanne), 2022, 9: 886853.
10	王士旭, 柯岩, 王贵齐. 人工智能在上消化道内镜检查质量控制中的应用 [J]. 中国肿瘤临床, 2021, 48(23): 1215-1219.
11	彭东阁, 万子叶, 卢宁. 人工智能在胃癌诊疗和患者预后预测中的应用现状及未来展望 [J]. 中国癌症杂志, 2025, 35(5): 496-504.
12	张倩, 曹云太, 王志洁, 等. 深度学习在早期胃癌内镜图像诊断中的研究进展 [J]. 实用医学杂志, 2025, 41(14): 2160-2166.
13	Ono S, Kawada K, Dohi O, et al. Linked color imaging focused on neoplasm detection in the upper gastrointestinal tract : a randomized trial [J]. Ann Intern Med, 2021, 174(1): 18-24.
14	Ling T, Wu L, Fu Y, et al. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy [J]. Endoscopy, 2021, 53(5): 469-477.
15	Abe S. Computer-aided endoscopic diagnosis of early gastric cancer on white light endoscopy: No detection, no characterization [J]. Dig Endosc, 2023, 35(4): 492-493.
16	李夏, 于红刚. 内镜诊断早期胃癌的新进展 [J]. 海南医学院学报, 2019, 25(5): 392-395.
17	Ueyama H, Kato Y, Akazawa Y, et al. Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging [J]. J Gastroenterol Hepatol, 2021, 36(2): 482-489.
18	Abe S, Makiguchi ME, Nonaka S, et al. Emerging texture and color enhancement imaging in early gastric cancer [J]. Dig Endosc, 2022, 34(4): 714-720.
19	Min M, Sun X, Bai J, et al. Diagnostic accuracy of linked colour imaging versus white light imaging for early gastric cancers: a prospective, multicentre, randomized controlled trial study [J]. Ann Med, 2022, 54(1): 3306-3314.
20	Ikenoyama Y, Hirasawa T, Ishioka M, et al. Detecting early gastric cancer: comparison between the diagnostic ability of convolutional neural networks and endoscopists [J]. Dig Endosc, 2021, 33(1): 141-150.
21	Jin J, Zhang Q, Dong B, et al. Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video) [J]. Front Oncol, 2022, 12: 927868.
22	Wu L, He X, Liu M, et al. Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial [J]. Endoscopy, 2021, 53(12): 1199-1207.
23	Abe S, Tomizawa Y, Saito Y. Can artificial intelligence be your angel to diagnose early gastric cancer in real clinical practice? [J]. Gastrointest Endosc, 2022, 95(4): 679-681.
24	He X, Wu L, Dong Z, et al. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos) [J]. Gastrointest Endosc, 2022, 95(4): 671-678.e4.
25	Oh Y, Bae GE, Kim KH, et al. Multi-scale hybrid vision transformer for learning gastric histology: AI-based decision support system for gastric cancer treatment [J]. IEEE J Biomed Health Inform, 2023, 27(8): 4143-4153.
26	Tang D, Ni M, Zheng C, et al. A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy [J]. Surg Endosc, 2022, 36(10): 7800-7810.
27	Deng Y, Qin HY, Zhou YY, et al. Artificial intelligence applications in pathological diagnosis of gastric cancer [J]. Heliyon, 2022, 8(12): e12431.
28	Ku Y, Ding H, Wang G. Efficient synchronous real-time CADe for multicategory lesions in gastroscopy by using multiclass detection model [J]. Biomed Res Int, 2022, 2022: 8504149.
29	Kim JH, Oh SI, Han SY, et al. An optimal artificial intelligence system for real-time endoscopic prediction of invasion depth in early gastric cancer [J]. Cancers (Basel), 2022, 14(23): 6000.
30	Uema R, Hayashi Y, Kizu T, et al. A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer [J]. J Gastroenterol, 2024, 59(7): 543-555.
31	Nam JY, Chung HJ, Choi KS, et al. Deep learning model for diagnosing gastric mucosal lesions using endoscopic images: development, validation, and method comparison [J]. Gastrointest Endosc, 2022, 95(2): 258-268.e10.
32	Zhu Y, Du L, Fu PY, et al. An automated video analysis system for retrospective assessment and real-time monitoring of endoscopic procedures (with Video) [J]. Bioengineering (Basel), 2024, 11(5): 445.
33	Goto A, Kubota N, Nishikawa J, et al. Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer [J]. Gastric Cancer, 2023, 26(1): 116-1122.
34	吴邦钰, 王志霄, 项景轩, 等. 基于卷积神经网络的胃癌病理图像分类诊断与分级识别[J/OL]. 南京医科大学学报(自然科学版), 1-13[2026-2-4].
35	Pagani W, Buysse T, Dua KS. Digital platforms, virtual reality, and augmented reality in gastrointestinal endoscopy training [J]. Clin Endosc, 2025, 58(5): 653-661.
36	Zhang Z, Chen BS, Du L, et al. Expert-AI collaborative training for novice endoscopists: a path to enhanced efficiency [J]. Bioengineering (Basel), 2025, 12(6): 582.
37	Li J, Zhu Y, Dong Z, et al. Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: a case-control study [J]. EClinicalMedicine, 2022, 46: 101366.
38	Hu H, Gong L, Dong D, et al. Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study [J]. Gastrointest Endosc, 2021, 93(6): 1333-1341.e3.
39	阚浩轩, 李博文, 王森, 等. 国际胃癌诊疗新趋势-基于第16届国际胃癌大会的前沿观察 [J]. 中国实用外科杂志, 2025, 45(11): 1244-1248.
40	Kim MJ, Kim SH, Kim SM, et al. The advent of domain adaptation into artificial intelligence for gastrointestinal endoscopy and medical imaging [J]. Diagnostics (Basel), 2023, 13(19): 3023.
41	Huang X, Qin M, Fang M, et al. The application of artificial intelligence in upper gastrointestinal cancers [J]. J Natl Cancer Cent, 2025, 5(2): 113-131.
42	You JG, Hernandez-Boussard T, Pfeffer MA, et al. Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications [J]. NPJ Digit Med, 2025, 8(1): 107.
43	Dong Z, Wang J, Li Y, et al. Publisher correction: explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy [J]. NPJ Digit Med, 2023, 6(1): 109.
44	Yao Z, Jin T, Mao B, et al. Construction and multicenter diagnostic verification of intelligent recognition system for endoscopic images from early gastric cancer based on Yolo-V3 algorithm [J]. Front Oncol, 2022, 12: 815951.
45	Martins BC, Moura RN, Kum AST, et al. Endoscopic imaging for the diagnosis of neoplastic and ore-neoplastic conditions of the stomach [J]. Cancers (Basel), 2023, 15(9): 2445.
46	Niemiec E. Will the EU medical device regulation help to improve the safety and performance of medical AI devices? [J]. Digit Health, 2022, 8: 20552076221089079.
47	Pattilachan TM, Christodoulou M, Ross S. Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer [J]. J Robot Surg, 2024, 18(1): 259.
48	Alghamdi BM, Rogers S, Roberman S, et al. Implementation and integration of a multidisciplinary pharmacogenomics service in an underserved integrated behavioral health clinic [J]. Front Pharmacol, 2025, 16: 1594032.
49	Gonçalves N, Chaves J, Marques-Sá I, et al. Early diagnosis of gastric cancer: Endoscopy and artificial intelligence [J]. Best Pract Res Clin Gastroenterol, 2025, 75: 101979.
50	Nabi Z, Manchu C, Reddy DN. Robotics in interventional endoscopy-evolution and the way forward [J]. Indian J Gastroenterol, 2024, 43(5): 966-975.

[1]	Haibo Xie, Zhiwen Wang, Heng Li. Integrated applications of imaging examinations and deep learning for early knee osteoarthritis [J]. Chinese Journal of Joint Surgery(Electronic Edition), 2026, 20(01): 97-103.
[2]	Ting Li, Chao Guo, Chenxi Li, Rouyan Li, Yihan Zhang, Yuhan Zhang, Yan Chen, Gaowa Siqin, Zhongcheng Gong. Deep learning-based automated segmentation and volumetric analysis of impacted maxillary canines [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 9-16.
[3]	Xiaofei Meng, Zhuohong Gong, Yaowen Wan, Peida Li, Qiqi Hu, Longshiyu Qiu, Hengyi Liu, Weili Xie. Preliminary exploration on the application of the classification of dental smile lines via deep learning and multimodal large language model [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 17-24.
[4]	Zhuyu Wang, Jingjing Quan. Artificial intelligence-assisted basic and clinical research in endodontics [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 25-33.
[5]	Siyi Lü, Yanqi Wang, Jun Qiu, Yujiang Chen, Jie Gao. Application of artificial intelligence image processing technology in stomatology [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 40-46.
[6]	Cuijun Li, Gengbin Cai, Xiaoyin Liang, Yongyi Wang, Xinyi Zhan, Peiming Gu. Application of artificial intelligence in dental nursing [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 47-50.
[7]	Zetao Chen, Longshiyu Qiu, Zhuohong Gong, Hengyi Liu, Peisheng Zeng, Mengru Shi. Challenges and strategies for artificial intelligence-based quantitative measurement in oral implant [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2026, 20(01): 1-8.
[8]	Tinglin Yang, Tao Huang. Progress and challenges in the application of artificial intelligence for evaluating thyroid nodules [J]. Chinese Archives of General Surgery(Electronic Edition), 2026, 20(01): 60-65.
[9]	Yufei Deng, Zhixin Wang, Ke Lou, Linxuan Zhang, Guichun Ma, Cuo Gang. Application of radiomics in precise diagnosis, efficacy evaluation, and treatment plan optimization of liver cancer [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(02): 172-180.
[10]	Chunyuan Yang, Xu Deng, Jingjing Wang, Cairang Yangdan, Wei Pan. Technical progress and clinical application of precise hepatectomy [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(02): 261-265.
[11]	Yue Tang, Jialu Chen, Delong Qin, Zonglong Li, Zhaohui Tang, Zhiwei Quan. Application and challenges of AI-based multimodal imaging in diagnosis and treatment of hepatocellular carcinoma [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(01): 4-9.
[12]	Xiaokun Chen, Shunda Du. Advances and challenges of radiomics in hepatocellular carcinoma [J]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2026, 16(02): 97-100.
[13]	Huiting Hou, Jing Wu, Liu Han, Yuyong Tan, Deliang Liu. Current status of early detection proportion of gastrointestinal carcinomas in public hospitals in Hunan Province [J]. Chinese Journal of Gastrointestinal Endoscopy(Electronic Edition), 2026, 13(01): 45-51.
[14]	Lu Wang, Yingcai Ma, Zhilan Liu, Xiaolin Li, Yanyan Lu. The application value and challenges of miniprobe endoscopic ultrasound in rectal neuroendocrine neoplasms [J]. Chinese Journal of Gastrointestinal Endoscopy(Electronic Edition), 2026, 13(01): 52-55.
[15]	Haonan Du, Guangjian Zhang, Jiawei Wang, Ting Liang, Ruiyang Suo, Jia Zhang. Application prospects of artificial intelligence in the diagnosis and treatment of pulmonary nodules [J]. Chinese Journal of Thoracic Surgery(Electronic Edition), 2026, 13(01): 64-73.

Please choose a citation manager

Content to export