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For: Ebigbo A, Mendel R, Probst A, Manzeneder J, Prinz F, de Souza LA Jr, Papa J, Palm C, Messmann H. Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus. Gut. 2020;69:615-616. [PMID: 31541004 DOI: 10.1136/gutjnl-2019-319460] [Cited by in Crossref: 43] [Cited by in F6Publishing: 40] [Article Influence: 14.3] [Reference Citation Analysis]
Number Citing Articles
1 Okagawa Y, Abe S, Yamada M, Oda I, Saito Y. Artificial Intelligence in Endoscopy. Dig Dis Sci 2021. [PMID: 34155567 DOI: 10.1007/s10620-021-07086-z] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Hamade N, Sharma P. 'Artificial intelligence in Barrett's Esophagus'. Ther Adv Gastrointest Endosc 2021;14:26317745211049964. [PMID: 34671724 DOI: 10.1177/26317745211049964] [Reference Citation Analysis]
3 Nawab K, Athwani R, Naeem A, Hamayun M, Wazir M. A Review of Applications of Artificial Intelligence in Gastroenterology. Cureus 2021;13:e19235. [PMID: 34877212 DOI: 10.7759/cureus.19235] [Reference Citation Analysis]
4 Chang K, Jackson CS, Vega KJ. Barrett's Esophagus: Diagnosis, Management, and Key Updates. Gastroenterol Clin North Am 2021;50:751-68. [PMID: 34717869 DOI: 10.1016/j.gtc.2021.08.009] [Reference Citation Analysis]
5 Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021;11:1719. [PMID: 34574060 DOI: 10.3390/diagnostics11091719] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 El-Nakeep S, El-Nakeep M. Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations. Artif Intell Gastroenterol 2021; 2(5): 124-132 [DOI: 10.35712/aig.v2.i5.124] [Reference Citation Analysis]
7 Arribas J, Antonelli G, Frazzoni L, Fuccio L, Ebigbo A, van der Sommen F, Ghatwary N, Palm C, Coimbra M, Renna F, Bergman JJGHM, Sharma P, Messmann H, Hassan C, Dinis-Ribeiro MJ. Standalone performance of artificial intelligence for upper GI neoplasia: a meta-analysis. Gut 2020:gutjnl-2020-321922. [PMID: 33127833 DOI: 10.1136/gutjnl-2020-321922] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
8 Lui TKL, Tsui VWM, Leung WK. Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointest Endosc. 2020;92:821-830.e9. [PMID: 32562608 DOI: 10.1016/j.gie.2020.06.034] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 8.5] [Reference Citation Analysis]
9 Tontini GE, Neumann H. Artificial intelligence: Thinking outside the box. Best Pract Res Clin Gastroenterol 2021;52-53:101720. [PMID: 34172247 DOI: 10.1016/j.bpg.2020.101720] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Byrne MF, Critchley-Thorne RJ. Move Over, Colon. It's Time for the Esophagus to Take Center Stage for Artificial Intelligence and Computer-Aided Detection of Barrett's! Gastroenterology 2021;161:802-4. [PMID: 34197829 DOI: 10.1053/j.gastro.2021.06.071] [Reference Citation Analysis]
11 Syed T, Doshi A, Guleria S, Syed S, Shah T. Artificial Intelligence and Its Role in Identifying Esophageal Neoplasia. Dig Dis Sci 2020;65:3448-55. [PMID: 33057945 DOI: 10.1007/s10620-020-06643-2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Li N, Jin SZ. Artificial intelligence and early esophageal cancer. Artif Intell Gastrointest Endosc 2021; 2(5): 198-210 [DOI: 10.37126/aige.v2.i5.198] [Reference Citation Analysis]
13 Schmitz R, Werner R, Repici A, Bisschops R, Meining A, Zornow M, Messmann H, Hassan C, Sharma P, Rösch T. Artificial intelligence in GI endoscopy: stumbling blocks, gold standards and the role of endoscopy societies. Gut 2021:gutjnl-2020-323115. [PMID: 33479051 DOI: 10.1136/gutjnl-2020-323115] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Sinonquel P, Eelbode T, Bossuyt P, Maes F, Bisschops R. Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy. Dig Endosc 2021;33:242-53. [PMID: 33145847 DOI: 10.1111/den.13888] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
15 Ebigbo A, Mendel R, Probst A, Meinikheim M, Byrne MF, Messmann H, Palm C. Multimodal imaging for detection and segmentation of Barrett's esophagus-related neoplasia using artificial intelligence. Endoscopy 2021. [PMID: 34933360 DOI: 10.1055/a-1704-7885] [Reference Citation Analysis]
16 Visaggi P, Barberio B, Ghisa M, Ribolsi M, Savarino V, Fassan M, Valmasoni M, Marchi S, de Bortoli N, Savarino E. Modern Diagnosis of Early Esophageal Cancer: From Blood Biomarkers to Advanced Endoscopy and Artificial Intelligence. Cancers (Basel) 2021;13:3162. [PMID: 34202763 DOI: 10.3390/cancers13133162] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
17 Hashimoto R, Requa J, Dao T, Ninh A, Tran E, Mai D, Lugo M, El-Hage Chehade N, Chang KJ, Karnes WE, Samarasena JB. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video). Gastrointest Endosc. 2020;91:1264-1271.e1. [PMID: 31930967 DOI: 10.1016/j.gie.2019.12.049] [Cited by in Crossref: 46] [Cited by in F6Publishing: 41] [Article Influence: 23.0] [Reference Citation Analysis]
18 Huang LM, Yang WJ, Huang ZY, Tang CW, Li J. Artificial intelligence technique in detection of early esophageal cancer. World J Gastroenterol 2020; 26(39): 5959-5969 [PMID: 33132647 DOI: 10.3748/wjg.v26.i39.5959] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
19 de Souza LA Jr, Passos LA, Mendel R, Ebigbo A, Probst A, Messmann H, Palm C, Papa JP. Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks. Comput Biol Med. 2020;126:104029. [PMID: 33059236 DOI: 10.1016/j.compbiomed.2020.104029] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
20 Chang K, Jackson CS, Vega KJ. Artificial intelligence in Barrett’s esophagus: A renaissance but not a reformation. Artif Intell Gastrointest Endosc 2020; 1(2): 28-32 [DOI: 10.37126/aige.v1.i2.28] [Reference Citation Analysis]
21 Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27(16): 1664-1690 [PMID: 33967550 DOI: 10.3748/wjg.v27.i16.1664] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Zhang SM, Wang YJ, Zhang ST. Accuracy of artificial intelligence-assisted detection of esophageal cancer and neoplasms on endoscopic images: A systematic review and meta-analysis. J Dig Dis 2021;22:318-28. [PMID: 33871932 DOI: 10.1111/1751-2980.12992] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
23 Ebigbo A, Palm C, Messmann H. Barrett esophagus: What to expect from Artificial Intelligence? Best Pract Res Clin Gastroenterol 2021;52-53:101726. [PMID: 34172253 DOI: 10.1016/j.bpg.2021.101726] [Reference Citation Analysis]
24 Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificial intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc 2020; 1(1): 6-18 [DOI: 10.37126/aige.v1.i1.6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
25 Bang CS, Lee JJ, Baik GH. Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy. Gastrointest Endosc 2021;93:1006-1015.e13. [PMID: 33290771 DOI: 10.1016/j.gie.2020.11.025] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
26 de Groof AJ, Struyvenberg MR, Fockens KN, van der Putten J, van der Sommen F, Boers TG, Zinger S, Bisschops R, de With PH, Pouw RE, Curvers WL, Schoon EJ, Bergman JJGHM. Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video). Gastrointest Endosc. 2020;91:1242-1250. [PMID: 31926965 DOI: 10.1016/j.gie.2019.12.048] [Cited by in Crossref: 29] [Cited by in F6Publishing: 28] [Article Influence: 14.5] [Reference Citation Analysis]
27 Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B, Neumann H. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc 2020;13:2631774520935220. [PMID: 32637935 DOI: 10.1177/2631774520935220] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
28 Li Q, Liu BR. Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer. Shijie Huaren Xiaohua Zazhi 2021; 29(24): 1389-1395 [DOI: 10.11569/wcjd.v29.i24.1389] [Reference Citation Analysis]
29 Correia FP, Lourenço LC. Artificial intelligence application in diagnostic gastrointestinal endoscopy - Deus ex machina? World J Gastroenterol 2021; 27(32): 5351-5361 [PMID: 34539137 DOI: 10.3748/wjg.v27.i32.5351] [Reference Citation Analysis]
30 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
31 Xu Y, Tan Y, Wang Y, Gao J, Wu D, Xu X. A Gratifying Step forward for the Application of Artificial Intelligence in the Field of Endoscopy: A Narrative Review. Surg Laparosc Endosc Percutan Tech 2020;31:254-63. [PMID: 33122593 DOI: 10.1097/SLE.0000000000000881] [Reference Citation Analysis]
32 de Souza LA Jr, Mendel R, Strasser S, Ebigbo A, Probst A, Messmann H, Papa JP, Palm C. Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box. Comput Biol Med 2021;135:104578. [PMID: 34171639 DOI: 10.1016/j.compbiomed.2021.104578] [Reference Citation Analysis]
33 Furnari M, Telese A, Hann A, Lisotti A, Boškoski I, Eusebi LH. New Devices for Endoscopic Treatments in Gastroenterology: A Narrative Review. Curr Drug Metab 2020;21:850-65. [PMID: 32703127 DOI: 10.2174/1389200221666200722145727] [Reference Citation Analysis]
34 Yang H, Hu B. Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases. Artif Intell Gastrointest Endosc 2021; 2(2): 25-35 [DOI: 10.37126/aige.v2.i2.25] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Iwagami H, Ishihara R, Aoyama K, Fukuda H, Shimamoto Y, Kono M, Nakahira H, Matsuura N, Shichijo S, Kanesaka T, Kanzaki H, Ishii T, Nakatani Y, Tada T. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol. 2021;36:131-136. [PMID: 32511793 DOI: 10.1111/jgh.15136] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
36 Lazăr DC, Avram MF, Faur AC, Goldiş A, Romoşan I, Tăban S, Cornianu M. The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future. Medicina (Kaunas) 2020;56:E364. [PMID: 32708343 DOI: 10.3390/medicina56070364] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
37 Honing J, di Pietro M. Surveillance for Barrett's esophagus: let's get the basics right. Gastrointest Endosc 2021:S0016-5107(21)01701-6. [PMID: 34863470 DOI: 10.1016/j.gie.2021.10.003] [Reference Citation Analysis]
38 Ebigbo A, Messmann H. Artificial intelligence in the upper GI tract: the future is fast approaching. Gastrointest Endosc 2021;93:1342-3. [PMID: 33715878 DOI: 10.1016/j.gie.2021.01.012] [Reference Citation Analysis]
39 Ebigbo A, Mendel R, Rückert T, Schuster L, Probst A, Manzeneder J, Prinz F, Mende M, Steinbrück I, Faiss S, Rauber D, de Souza LA Jr, Papa JP, Deprez PH, Oyama T, Takahashi A, Seewald S, Sharma P, Byrne MF, Palm C, Messmann H. Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study. Endoscopy 2021;53:878-83. [PMID: 33197942 DOI: 10.1055/a-1311-8570] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
40 Tanabe S, Perkins EJ, Ono R, Sasaki H. Artificial intelligence in gastrointestinal diseases. Artif Intell Gastroenterol 2021; 2(3): 69-76 [DOI: 10.35712/aig.v2.i3.69] [Reference Citation Analysis]
41 Lazăr DC, Avram MF, Faur AC, Romoşan I, Goldiş A. The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions. Gastroenterol Rep (Oxf) 2021;9:185-204. [PMID: 34316369 DOI: 10.1093/gastro/goab008] [Reference Citation Analysis]
42 Sinonquel P, Bisschops R. Striving for quality improvement: can artificial intelligence help? Best Pract Res Clin Gastroenterol 2021;52-53:101722. [PMID: 34172249 DOI: 10.1016/j.bpg.2020.101722] [Reference Citation Analysis]
43 Yu H, Singh R, Shin SH, Ho KY. Artificial intelligence in upper GI endoscopy - current status, challenges and future promise. J Gastroenterol Hepatol. 2021;36:20-24. [PMID: 33448515 DOI: 10.1111/jgh.15354] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
44 Kolb JM, Wani S. Endoscopic eradication therapy for Barrett's oesophagus: state of the art. Curr Opin Gastroenterol 2020;36:351-8. [PMID: 32487852 DOI: 10.1097/MOG.0000000000000650] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
45 Mathews SC, Sakulsaengprapha V. Digital Health Landscape in Gastroenterology and Hepatology. Clin Gastroenterol Hepatol 2021;19:421-424.e2. [PMID: 33157314 DOI: 10.1016/j.cgh.2020.11.001] [Reference Citation Analysis]
46 Mohan B, Kumar S, Xi H, Ma S, Tao Z, Xing T, You H, Zhang Y, Ren P. Fabricated Metal-Organic Frameworks (MOFs) as luminescent and electrochemical biosensors for cancer biomarkers detection. Biosens Bioelectron 2022;197:113738. [PMID: 34740120 DOI: 10.1016/j.bios.2021.113738] [Reference Citation Analysis]
47 Maslyonkina KS, Konyukova AK, Alexeeva DY, Sinelnikov MY, Mikhaleva LM. Barrett's esophagus: The pathomorphological and molecular genetic keystones of neoplastic progression. Cancer Med 2021. [PMID: 34870375 DOI: 10.1002/cam4.4447] [Reference Citation Analysis]
48 Zhuang H, Bao A, Tan Y, Wang H, Xie Q, Qiu M, Xiong W, Liao F. Application and prospect of artificial intelligence in digestive endoscopy. Expert Rev Gastroenterol Hepatol 2021;:1-11. [PMID: 34937459 DOI: 10.1080/17474124.2022.2020646] [Reference Citation Analysis]
49 Suzuki H, Yoshitaka T, Yoshio T, Tada T. Artificial intelligence for cancer detection of the upper gastrointestinal tract. Dig Endosc 2021;33:254-62. [PMID: 33222330 DOI: 10.1111/den.13897] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
50 Yan T, Wong PK, Qin YY. Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review. World J Gastroenterol 2021; 27(20): 2531-2544 [PMID: 34092974 DOI: 10.3748/wjg.v27.i20.2531] [Reference Citation Analysis]
51 Liu Y. Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step? World J Gastroenterol 2021; 27(14): 1392-1405 [PMID: 33911463 DOI: 10.3748/wjg.v27.i14.1392] [Reference Citation Analysis]
52 Sutton RA, Sharma P. Overcoming barriers to implementation of artificial intelligence in gastroenterology. Best Pract Res Clin Gastroenterol 2021;52-53:101732. [PMID: 34172254 DOI: 10.1016/j.bpg.2021.101732] [Reference Citation Analysis]