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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy
Tingsheng Ling, Lianlian Wu, Yiwei Fu, et al.
Endoscopy (2020) Vol. 53, Iss. 05, pp. 469-477
Closed Access | Times Cited: 73

Showing 1-25 of 73 citing articles:

Efficient Gastrointestinal Disease Classification Using Pretrained Deep Convolutional Neural Network
Muhammad Nouman Noor, Muhammad Nazir, Sajid Ali Khan, et al.
Electronics (2023) Vol. 12, Iss. 7, pp. 1557-1557
Open Access | Times Cited: 42
Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology
Zhe Wang, Yang Liu, Xing Niu
Seminars in Cancer Biology (2023) Vol. 93, pp. 83-96
Closed Access | Times Cited: 41
Artificial Intelligence in Endoscopy
Yutaka Okagawa, Seiichiro Abe, Masayoshi Yamada, et al.
Digestive Diseases and Sciences (2021) Vol. 67, Iss. 5, pp. 1553-1572
Closed Access | Times Cited: 83
Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos)
Lianlian Wu, Jing Wang, Xinqi He, et al.
Gastrointestinal Endoscopy (2021) Vol. 95, Iss. 1, pp. 92-104.e3
Closed Access | Times Cited: 59
Kyoto international consensus report on anatomy, pathophysiology and clinical significance of the gastro-oesophageal junction
Kentaro Sugano, Stuart J. Spechler, Emad El‐Omar, et al.
Gut (2022), pp. gutjnl-327281
Open Access | Times Cited: 41
A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer
Ryotaro Uema, Yoshito Hayashi, Takashi Kizu, et al.
Journal of Gastroenterology (2024) Vol. 59, Iss. 7, pp. 543-555
Open Access | Times Cited: 8
Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects
Changda Lei, Wenqiang Sun, Kun Wang, et al.
Annals of Medicine (2025) Vol. 57, Iss. 1
Open Access | Times Cited: 1
Artificial Intelligence in Gastrointestinal Cancer Research: Image Learning Advances and Applications
Shengyuan Zhou, Yi Xie, Xujiao Feng, et al.
Cancer Letters (2025) Vol. 614, pp. 217555-217555
Closed Access | Times Cited: 1
Artificial Intelligence and Deep Learning for Upper Gastrointestinal Neoplasia
Prateek Sharma, Cesare Hassan
Gastroenterology (2021) Vol. 162, Iss. 4, pp. 1056-1066
Open Access | Times Cited: 45
Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos)
Xinqi He, Lianlian Wu, Zehua Dong, et al.
Gastrointestinal Endoscopy (2021) Vol. 95, Iss. 4, pp. 671-678.e4
Closed Access | Times Cited: 38
Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis
Kailin Jiang, Xiaotao Jiang, Jinglin Pan, et al.
Frontiers in Medicine (2021) Vol. 8
Open Access | Times Cited: 34
Identification of gastric cancer with convolutional neural networks: a systematic review
Yuxue Zhao, Bo Hu, Ying Wang, et al.
Multimedia Tools and Applications (2022) Vol. 81, Iss. 8, pp. 11717-11736
Open Access | Times Cited: 26
Artificial intelligence in gastric cancer: applications and challenges
Runnan Cao, Lei Tang, Mengjie Fang, et al.
Gastroenterology report (2022) Vol. 10
Open Access | Times Cited: 23
Deep learning for gastroscopic images: computer-aided techniques for clinicians
Ziyi Jin, Tianyuan Gan, Peng Wang, et al.
BioMedical Engineering OnLine (2022) Vol. 21, Iss. 1
Open Access | Times Cited: 22
Medical image identification methods: A review
Juan Li, Pan Jiang, Qing An, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107777-107777
Closed Access | Times Cited: 14
Localization and Classification of Gastrointestinal Tract Disorders Using Explainable AI from Endoscopic Images
Muhammad Nouman Noor, Muhammad Nazir, Sajid Ali Khan, et al.
Applied Sciences (2023) Vol. 13, Iss. 15, pp. 9031-9031
Open Access | Times Cited: 13
Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging
Yusuke Horiuchi, Toshiaki Hirasawa, Junko Fujisaki
Clinical Endoscopy (2024) Vol. 57, Iss. 1, pp. 11-17
Open Access | Times Cited: 5
Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images
Shaha Al‐Otaibi, Amjad Rehman, Muhammad Mujahid, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e1902-e1902
Open Access | Times Cited: 5
Enhancing Lymph Node Metastasis Risk Prediction in Early Gastric Cancer Through the Integration of Endoscopic Images and Real-World Data in a Multimodal AI Model
Dong Hoon Kang, Han Jo Jeon, Jie‐Hyun Kim, et al.
Cancers (2025) Vol. 17, Iss. 5, pp. 869-869
Open Access
A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation
Jing Wang, Ying Li, Shuyu Li, et al.
Endoscopy (2023) Vol. 56, Iss. 04, pp. 260-270
Closed Access | Times Cited: 12
Evaluation of deep learning methods for early gastric cancer detection using gastroscopic images
Xiufeng Su, Qingshan Liu, Xiaozhong Gao, et al.
Technology and Health Care (2023) Vol. 31, pp. 313-322
Open Access | Times Cited: 11
Diagnostic accuracy of convolutional neural network–based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis
Fang Xie, Keqiang Zhang, Feng Li, et al.
Gastrointestinal Endoscopy (2021) Vol. 95, Iss. 4, pp. 599-609.e7
Closed Access | Times Cited: 23
A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy
Dehua Tang, Muhan Ni, Chang Zheng, et al.
Surgical Endoscopy (2022) Vol. 36, Iss. 10, pp. 7800-7810
Closed Access | Times Cited: 16
A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video)
Hongliu Du, Zehua Dong, Lianlian Wu, et al.
Gastric Cancer (2022) Vol. 26, Iss. 2, pp. 275-285
Open Access | Times Cited: 16
Deep learning in precision medicine and focus on glioma
Yihao Liu, Minghua Wu
Bioengineering & Translational Medicine (2023) Vol. 8, Iss. 5
Open Access | Times Cited: 10
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