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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:

Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett’s esophagus (with video)
Rintaro Hashimoto, James Requa, Tyler Dao, et al.
Gastrointestinal Endoscopy (2020) Vol. 91, Iss. 6, pp. 1264-1271.e1
Closed Access | Times Cited: 186

Showing 1-25 of 186 citing articles:

The Integration of Artificial Intelligence into Clinical Practice
Vangelis Karalis
Applied Biosciences (2024) Vol. 3, Iss. 1, pp. 14-44
Open Access | Times Cited: 83
Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement
Helmut Messmann, Raf Bisschops, Giulio Antonelli, et al.
Endoscopy (2022) Vol. 54, Iss. 12, pp. 1211-1231
Open Access | Times Cited: 80
Diagnosis and management of Barrett esophagus: European Society of Gastrointestinal Endoscopy (ESGE) Guideline
Bas L. Weusten, Raf Bisschops, Mário Dinis‐Ribeiro, et al.
Endoscopy (2023) Vol. 55, Iss. 12, pp. 1124-1146
Open Access | Times Cited: 68
Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection
Danqing Xu, Yiquan Wu
Sensors (2020) Vol. 20, Iss. 15, pp. 4276-4276
Open Access | Times Cited: 116
Artificial intelligence in gastroenterology: A state-of-the-art review
Paul T. Kröner, Megan Engels, Benjamin S. Glicksberg, et al.
World Journal of Gastroenterology (2021) Vol. 27, Iss. 40, pp. 6794-6824
Open Access | Times Cited: 91
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
Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force
Tyler M. Berzin, Sravanthi Parasa, Michael B. Wallace, et al.
Gastrointestinal Endoscopy (2020) Vol. 92, Iss. 4, pp. 951-959
Open Access | Times Cited: 78
Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis
Julia Arribas, Giulio Antonelli, Leonardo Frazzoni, et al.
Gut (2020) Vol. 70, Iss. 8, pp. 1458-1468
Closed Access | Times Cited: 70
Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity
Eugenia Uche-Anya, Adjoa Anyane‐Yeboa, Tyler M. Berzin, et al.
Gut (2022) Vol. 71, Iss. 9, pp. 1909-1915
Open Access | Times Cited: 65
Systematic review with meta‐analysis: artificial intelligence in the diagnosis of oesophageal diseases
Pierfrancesco Visaggi, Brigida Barberio, Darío Gregori, et al.
Alimentary Pharmacology & Therapeutics (2022) Vol. 55, Iss. 5, pp. 528-540
Open Access | Times Cited: 45
Development and validation of artificial neural networks model for detection of Barrett’s neoplasia: a multicenter pragmatic nonrandomized trial (with video)
Mohamed Abdelrahim, Masahiro Saiko, Naoto Maeda, et al.
Gastrointestinal Endoscopy (2022) Vol. 97, Iss. 3, pp. 422-434
Open Access | Times Cited: 42
Towards a robust and compact deep learning system for primary detection of early Barrett’s neoplasia: Initial image‐based results of training on a multi‐center retrospectively collected data set
Kiki Fockens, Jelmer B. Jukema, Tim Boers, et al.
United European Gastroenterology Journal (2023) Vol. 11, Iss. 4, pp. 324-336
Open Access | Times Cited: 23
A deep learning system for detection of early Barrett's neoplasia: a model development and validation study
Kiki Fockens, M. R. Jong, J-Wouter Jukema, et al.
The Lancet Digital Health (2023) Vol. 5, Iss. 12, pp. e905-e916
Open Access | Times Cited: 22
Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett’s esophagus: a tandem randomized and video trial
M. Meinikheim, Robert Mendel, Christoph Palm, et al.
Endoscopy (2024) Vol. 56, Iss. 09, pp. 641-649
Open Access | Times Cited: 10
Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies
Tim J. M. Jaspers, Tim G. W. Boers, Carolus H. J. Kusters, et al.
Medical Image Analysis (2024) Vol. 94, pp. 103157-103157
Open Access | Times Cited: 8
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 in gastrointestinal endoscopy
Rahul Pannala, Kumar Krishnan, Joshua Melson, et al.
VideoGIE (2020) Vol. 5, Iss. 12, pp. 598-613
Open Access | Times Cited: 66
Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy
Chang Seok Bang, Jae Jun Lee, Gwang Ho Baik
Gastrointestinal Endoscopy (2020) Vol. 93, Iss. 5, pp. 1006-1015.e13
Open Access | Times Cited: 62
Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of artificial intelligence: a pilot study
Alanna Ebigbo, Robert Mendel, T. Rückert, et al.
Endoscopy (2020) Vol. 53, Iss. 09, pp. 878-883
Closed Access | Times Cited: 56
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
Endoscopic Images by a Single-Shot Multibox Detector for the Identification of Early Cancerous Lesions in the Esophagus: A Pilot Study
Yao‐Kuang Wang, Hao-Yi Syu, Yi‐Hsun Chen, et al.
Cancers (2021) Vol. 13, Iss. 2, pp. 321-321
Open Access | Times Cited: 42
A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks
Mohamed Hussein, Juana González‐Bueno Puyal, David Lines, et al.
United European Gastroenterology Journal (2022) Vol. 10, Iss. 6, pp. 528-537
Open Access | Times Cited: 34
National Institute for Health and Care Excellence (NICE) guidance on monitoring and management of Barrett’s oesophagus and stage I oesophageal adenocarcinoma
Massimiliano di Pietro, Nigel Trudgill, Melina Vasileiou, et al.
Gut (2024) Vol. 73, Iss. 6, pp. 897-909
Open Access | Times Cited: 7
Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology
Sravanthi Parasa, Tyler M. Berzin, Cadman L. Leggett, et al.
Gastrointestinal Endoscopy (2024)
Closed Access | Times Cited: 7
Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques
Hari Mohan, Joon Yoo, Abdul Razaque
Expert Systems with Applications (2024) Vol. 255, pp. 124838-124838
Closed Access | Times Cited: 7
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