Peer-Review Report of This Article
CrossCheck and Google Search of This Article
Academic Rules and Norms of This Article
Citation of this article
Corresponding Author of This Article
Research Domain of This Article
Article-Type of This Article
Open-Access Policy of This Article
Times Cited Counts in Google of This Article
Number of Hits and Downloads for This Article
- Total Article Views (12883)
All Articles published online
|
Featured Article
|
Publishing Process of This Article
|
Jan 21, 2021 (publication date) through May 2, 2026
Times Cited of This Article
Journal Information of This Article
Publication Name
World Journal of Gastroenterology
ISSN
1007-9327
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
| Li B, Cai SL, Tan WM, Li JC, Yalikong A, Feng XS, Yu HH, Lu PX, Feng Z, Yao LQ, Zhou PH, Yan B, Zhong YS. Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging. World J Gastroenterol 2021; 27(3): 281-293 [PMID: 33519142 DOI: 10.3748/wjg.v27.i3.281] |
|
|
|
| All content on this site: Copyright © 1993-2026 Baishideng Publishing Group Inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply. | |
| For: | Li B, Cai SL, Tan WM, Li JC, Yalikong A, Feng XS, Yu HH, Lu PX, Feng Z, Yao LQ, Zhou PH, Yan B, Zhong YS. Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging. World J Gastroenterol 2021; 27(3): 281-293 [PMID: 33519142 DOI: 10.3748/wjg.v27.i3.281] |
|---|---|
| URL: | https://www.wjgnet.com/1007-9327/full/v27/i3/281.htm |
| Number | Citing Articles |
| 1 |
浩 王. Research Progress on Artificial Intelligence-Assisted Endoscopic Identification of Esophageal Cancer. Advances in Clinical Medicine 2024; 14(09): 715 doi: 10.12677/acm.2024.1492521
|
| 2 |
Eyad Gadour, Bodour Raheem, Antonio Facciorusso. A critical review of technical progress, clinical heterogeneity, and implementation challenges of artificial intelligence in digestive endoscopy. Expert Review of Gastroenterology & Hepatology 2026; 20(4): 335 doi: 10.1080/17474124.2026.2646299
|
| 3 |
Kotaro Waki, Katsuya Nagaoka, Keishi Okubo, Masato Kiyama, Ryosuke Gushima, Kento Ohno, Munenori Honda, Akira Yamasaki, Kenshi Matsuno, Yoki Furuta, Hideaki Miyamoto, Hideaki Naoe, Motoki Amagasaki, Yasuhito Tanaka. Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images. Scientific Reports 2025; 15(1) doi: 10.1038/s41598-025-86829-8
|
| 4 |
Si-yan Yan, Xin-yu Fu, Yan Yang, Liu-yi Jia, Jia-wei Liang, Ying-hui Li, Ling-ling Yan, Ying Zhou, Xian-bin Zhou, Shao-wei Li, Xin-li Mao. Artificial intelligence in early screening for esophageal squamous cell carcinoma. Best Practice & Research Clinical Gastroenterology 2025; 75: 102004 doi: 10.1016/j.bpg.2025.102004
|
| 5 |
Sayaka Nagao, Yasuhiro Tani, Junichi Shibata, Yosuke Tsuji, Tomohiro Tada, Ryu Ishihara, Mitsuhiro Fujishiro. Implementation of artificial intelligence in upper gastrointestinal endoscopy. DEN Open 2022; 2(1) doi: 10.1002/deo2.72
|
| 6 |
Qian-Qian Meng, Ye Gao, Han Lin, Tian-Jiao Wang, Yan-Rong Zhang, Jian Feng, Zhao-Shen Li, Lei Xin, Luo-Wei Wang. Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma. World Journal of Gastroenterology 2022; 28(37): 5483-5493 doi: 10.3748/wjg.v28.i37.5483
|
| 7 |
Piyush Nathani, Prateek Sharma. Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach. Gastrointestinal Endoscopy Clinics of North America 2025; 35(2): 319 doi: 10.1016/j.giec.2024.10.003
|
| 8 |
Shu-Qi Ren, Jin-Man Chen, Chuang Cai. Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis. World Journal of Gastroenterology 2025; 31(36): 110742 doi: 10.3748/wjg.v31.i36.110742
|
| 9 |
Qing Li, Bing-Rong Liu. Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer. World Chinese Journal of Digestology 2021; 29(24): 1389 doi: 10.11569/wcjd.v29.i24.1389
|
| 10 |
Xiang-Lei Yuan, Xian-Hui Zeng, Wei Liu, Yi Mou, Wan-Hong Zhang, Zheng-Duan Zhou, Xin Chen, Yan-Xing Hu, Bing Hu. Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video). Gastrointestinal Endoscopy 2023; 97(4): 664 doi: 10.1016/j.gie.2022.12.003
|
| 11 |
Yongkang Tao, Long Fang, Geng Qin, Yingying Xu, Shuang Zhang, Xiangrong Zhang, Shiyu Du. Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer. Thoracic Cancer 2024; 15(16): 1296 doi: 10.1111/1759-7714.15261
|
| 12 |
Paul T Kröner, Megan ML Engels, Benjamin S Glicksberg, Kipp W Johnson, Obaie Mzaik, Jeanin E van Hooft, Michael B Wallace, Hashem B El-Serag, Chayakrit Krittanawong. Artificial intelligence in gastroenterology: A state-of-the-art review. World Journal of Gastroenterology 2021; 27(40): 6794-6824 doi: 10.3748/wjg.v27.i40.6794
|
| 13 |
Hemant Goyal, Syed A. A. Sherazi, Rupinder Mann, Zainab Gandhi, Abhilash Perisetti, Muhammad Aziz, Saurabh Chandan, Jonathan Kopel, Benjamin Tharian, Neil Sharma, Nirav Thosani. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers 2021; 13(21): 5494 doi: 10.3390/cancers13215494
|
| 14 |
Min Liang, Chunhong Xu, Xinyan Zhang, Zongwang Zhang, Junli Cao. Effect of anesthesia assistance on the detection rate of precancerous lesions and early esophageal squamous cell cancer in esophagogastroduodenoscopy screening: A retrospective study based on propensity score matching. Frontiers in Medicine 2023; 10 doi: 10.3389/fmed.2023.1039979
|
| 15 |
Md. Mohaimenul Islam, Tahmina Nasrin Poly, Bruno Andreas Walther, Chih-Yang Yeh, Shabbir Seyed-Abdul, Yu-Chuan (Jack) Li, Ming-Chin Lin. Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis. Cancers 2022; 14(23): 5996 doi: 10.3390/cancers14235996
|
| 16 |
Yunhao Li, Kimberly Ho, Thomas Ka-Luen Lui, Wai K. Leung. Comparison of Image-Enhanced Endoscopy Techniques for Colorectal Lesion Detection and Characterization: A Network Meta-Analysis of Randomized Controlled Trials. American Journal of Gastroenterology 2026; 121(4): 835 doi: 10.14309/ajg.0000000000003848
|
| 17 |
Ken Kurisaki, Shinichiro Kobayashi, Taro Akashi, Yasuhiko Nakao, Masayuki Fukumoto, Kaito Tasaki, Tomohiko Adachi, Susumu Eguchi, Kengo Kanetaka. Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma. World Journal of Gastrointestinal Oncology 2026; 18(1): 111357 doi: 10.4251/wjgo.v18.i1.111357
|
| 18 |
Yuwei Pan, Lanying He, Weiqing Chen, Yongtao Yang. The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma. Frontiers in Oncology 2023; 13 doi: 10.3389/fonc.2023.1198941
|
| 19 |
Jun-Qi Zhang, Jun-Jie Mi, Rong Wang. Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis. World Journal of Gastrointestinal Oncology 2023; 15(11): 1998-2016 doi: 10.4251/wjgo.v15.i11.1998
Abstract(676) |
Core Tip(738) |
Full Article(HTML)(2462)
|
Full Article with Cover (PDF)-3503K(191)
|
Full Article (Word)-2629K(15)
|
Audio-394K(12)
|
Peer-Review Report-206K(121)
|
Answering Reviewers-170K(120)
|
Full Article (PDF)-3212K(188)
|
Full Article (XML)-231K(130)
|
Times Cited (6)
|
Total Visits (8576)
|
Open
|
| 20 |
Suigu Tang, Xiaoyuan Yu, Chak Fong Cheang, Xiaoyu Ji, Hon Ho Yu, I Cheong Choi. CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images. Computer Methods and Programs in Biomedicine 2023; 231: 107399 doi: 10.1016/j.cmpb.2023.107399
|
| 21 |
Pierfrancesco Visaggi, Nicola de Bortoli, Brigida Barberio, Vincenzo Savarino, Roberto Oleas, Emma M. Rosi, Santino Marchi, Mentore Ribolsi, Edoardo Savarino. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. Journal of Clinical Gastroenterology 2022; 56(1): 23 doi: 10.1097/MCG.0000000000001629
|
| 22 |
De Luo, Fei Kuang, Juan Du, Mengjia Zhou, Xiangdong Liu, Xinchen Luo, Yong Tang, Bo Li, Song Su. Artificial Intelligence–Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.855175
|
| 23 |
Ji-Han Qi, Shi-Ling Huang, Shi-Zhu Jin. Novel milestones for early esophageal carcinoma: From bench to bed. World Journal of Gastrointestinal Oncology 2024; 16(4): 1104-1118 doi: 10.4251/wjgo.v16.i4.1104
Abstract(630) |
Core Tip(646) |
Full Article(HTML)(2390)
|
Full Article with Cover (PDF)-4949K(295)
|
Audio-95K(10)
|
Peer-Review Report-310K(123)
|
Answering Reviewers-1577K(140)
|
Supplementary Material-993K(125)
|
Full Article (PDF)-4723K(201)
|
Full Article (XML)-268K(137)
|
Times Cited (7)
|
Total Visits (6694)
|
Open
|
| 24 |
Charalampos Theocharopoulos, Spyridon Davakis, Dimitrios C. Ziogas, Achilleas Theocharopoulos, Dimitra Foteinou, Adam Mylonakis, Ioannis Katsaros, Helen Gogas, Alexandros Charalabopoulos. Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer. Cancers 2024; 16(19): 3285 doi: 10.3390/cancers16193285
|
| 25 |
Wan-Yue Zhang, Yong-Jian Chang, Rui-Hua Shi. Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era. World Journal of Gastroenterology 2024; 30(39): 4267-4280 doi: 10.3748/wjg.v30.i39.4267
|
| 26 |
Hari Mohan Rai, Joon Yoo, Abdul Razaque. Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques. Expert Systems with Applications 2024; 255: 124838 doi: 10.1016/j.eswa.2024.124838
|
| 27 |
Chaoyi Shi, Shunhai Zhou, Xuanran Chen, Diyun Shen, GeSang ZhuoMa, Mingzhi Feng, Yan Sun, Jun Zhang. The impact of photographic image quantity on early upper gastrointestinal cancer detection during esophagogastroduodenoscopy. Scientific Reports 2025; 16(1) doi: 10.1038/s41598-025-29090-3
|
| 28 |
月 徐. Advances in Artificial Intelligence-Assisted Endoscopic Diagnosis of Esophageal Squamous Cell Carcinoma. Advances in Clinical Medicine 2026; 16(03): 3788 doi: 10.12677/acm.2026.1631187
|
| 29 |
Nuoya Zhou, Xianglei Yuan, Wei Liu, Qi Luo, Ruide Liu, Bing Hu. Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions. Chinese Medical Journal 2025; 138(12): 1387 doi: 10.1097/CM9.0000000000003490
|
| 30 |
Ming-Wun Wong, Benjamin D. Rogers, Min-Xiang Liu, Wei-Yi Lei, Tso-Tsai Liu, Chih-Hsun Yi, Jui-Sheng Hung, Shu-Wei Liang, Chiu-Wang Tseng, Jen-Hung Wang, Ping-An Wu, Chien-Lin Chen. Application of Artificial Intelligence in Measuring Novel pH-Impedance Metrics for Optimal Diagnosis of GERD. Diagnostics 2023; 13(5): 960 doi: 10.3390/diagnostics13050960
|
| 31 |
T. A. Sadulaeva, L. A. Edilgireeva, M. B. Bimurzaeva, A. O. Morozov. Use of artificial intelligence in diagnostic cystoscopy of bladder cancer. Cancer Urology 2023; 19(2): 146 doi: 10.17650/1726-9776-2023-19-2-148-152
|
| 32 |
Shaleen Vasavada, Sharmila Anandasabapathy. Image Enhanced Endoscopy in Esophageal Squamous Cell Carcinoma. Foregut: The Journal of the American Foregut Society 2024; 4(1): 82 doi: 10.1177/26345161231211752
|
| 33 |
Naoki Aoyama, Keiichiro Nakajo, Maasa Sasabe, Atsushi Inaba, Yuki Nakanishi, Hiroshi Seno, Tomonori Yano. Effects of artificial intelligence assistance on endoscopist performance: Comparison of diagnostic performance in superficial esophageal squamous cell carcinoma detection using video‐based models. DEN Open 2026; 6(1) doi: 10.1002/deo2.70083
|
| 34 |
Pierfrancesco Visaggi, Brigida Barberio, Dario Gregori, Danila Azzolina, Matteo Martinato, Cesare Hassan, Prateek Sharma, Edoardo Savarino, Nicola de Bortoli. Systematic review with meta‐analysis: artificial intelligence in the diagnosis of oesophageal diseases. Alimentary Pharmacology & Therapeutics 2022; 55(5): 528 doi: 10.1111/apt.16778
|
| 35 |
Silvia Pecere, Sebastian Manuel Milluzzo, Gianluca Esposito, Emanuele Dilaghi, Andrea Telese, Leonardo Henry Eusebi. Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases. Diagnostics 2021; 11(9): 1575 doi: 10.3390/diagnostics11091575
|
| 36 |
Bing Li, Yan-Yun Du, Wei-Min Tan, Dong-Li He, Zhi-Peng Qi, Hon-Ho Yu, Qiang Shi, Zhong Ren, Ming-Yan Cai, Bo Yan, Shi-Lun Cai, Yun-Shi Zhong. Effect of computer aided detection system on esophageal neoplasm diagnosis in varied levels of endoscopists. npj Digital Medicine 2025; 8(1) doi: 10.1038/s41746-025-01532-2
|
| 37 |
Amir Rafati Fard, Simon C. Williams, Kieran J. Smith, Jasneet K. Dhaliwal, Tomas Ferreira, Adrito Das, Joachim Starup-Hansen, John G. Hanrahan, Chan Hee Koh, Danyal Z. Khan, Danail Stoyanov, Hani J. Marcus. Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis. npj Digital Medicine 2026; 9(1) doi: 10.1038/s41746-026-02401-2
|
| 38 |
Nadia Guidozzi, Nainika Menon, Swathikan Chidambaram, Sheraz Rehan Markar. The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis. Diseases of the Esophagus 2023; 36(12) doi: 10.1093/dote/doad048
|
| 39 |
Seth A Gross, Matthew O'Mara, Robert J Duve, Jonathan S Galati. Artificial intelligence in gastroenterology: A narrative review. Artificial Intelligence in Gastroenterology 2022; 3(5): 117-141 doi: 10.35712/aig.v3.i5.117
|