Research trends on artificial intelligence and endoscopy in digestive diseases: A bibliometric analysis from 1990 to 2022

Table 1 Top 10 most cited articles in the field of AI and endoscopy from 1990-2022

Rank	Journal	Title	Citations of Web of Science	Affiliations	Ref.
1	Gastric Cancer 2018; 21: 653-660	Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images	323	Tada Tomohiro Inst Gastroenterol and Proctol, Saitama, Japan	Hirasawa et al[26], 2018
2	GUT 2019; 68: 1813-1819	Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study	307	Sichuan Provincial People’s Hospital, China	Wang et al[23], 2019
3	Annals of Internal Medicine 2018; 169: 357-366	Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy A Prospective Study	227	Showa University, Japan	Mori et al[39], 2018
4	Gastroenterology 2018; 154: 568-575	Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis	208	Triservice General Hospital, National Defense Medical Center, Taiwan	Chen et al[40] , 2018
5	Gastrointestinal Endoscopy 2019; 89: 25-32	Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks	194	Japanese Foundation for Cancer Research, Japan	Horie et al[28], 2019
6	IEEE Journal of Biomedical and Health Informatics 2017; 21: 41-47	Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain	191	Chinese University of Hong Kong, China	Zhang et al[35], 2017
7	Gastroenterology 2013; 144: 81-91	Real-Time Optical Biopsy of Colon Polyps With Narrow Band Imaging in Community Practice Does Not Yet Meet Key Thresholds for Clinical Decisions	169	Stanford University, United States	Ladabaum et al[41], 2013
8	Gastroenterology 2020; 159: 512-520	Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial	155	Humanitas University, IRCCS Humanitas Research Hospital, Italy	Repici et al[42], 2020
9	Gastrointestinal Endoscopy 2019; 89: 806-815	Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy	150	Fudan University, China	Zhu et al[43], 2019
10	Lancet Gastroenterology and Hepatology 2020; 5: 343-351	Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study	145	Sichuan Provincial People’s Hospital, China	Wang et al[44], 2020