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For: Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68:1813-1819. [PMID: 30814121 DOI: 10.1136/gutjnl-2018-317500] [Cited by in Crossref: 241] [Cited by in F6Publishing: 206] [Article Influence: 80.3] [Reference Citation Analysis]
Number Citing Articles
1 Sung JJ, Poon NC. Artificial intelligence in gastroenterology: where are we heading? Front Med. 2020;14:511-517. [PMID: 32458189 DOI: 10.1007/s11684-020-0742-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
2 Zhou G, Xiao X, Tu M, Liu P, Yang D, Liu X, Zhang R, Li L, Lei S, Wang H, Song Y, Wang P. Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy.PloS One. 2020;15:e0231880. [PMID: 32315365 DOI: 10.1371/journal.pone.0231880] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
3 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]
4 Liu P, Wang P, Glissen Brown JR, Berzin TM, Zhou G, Liu W, Xiao X, Chen Z, Zhang Z, Zhou C, Lei L, Xiong F, Li L, Liu X. The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study.Therap Adv Gastroenterol. 2020;13:1756284820979165. [PMID: 33403003 DOI: 10.1177/1756284820979165] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
5 Spadaccini M, Iannone A, Maselli R, Badalamenti M, Desai M, Chandrasekar VT, Patel HK, Fugazza A, Pellegatta G, Galtieri PA, Lollo G, Carrara S, Anderloni A, Rex DK, Savevski V, Wallace MB, Bhandari P, Roesch T, Gralnek IM, Sharma P, Hassan C, Repici A. Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis. Lancet Gastroenterol Hepatol 2021:S2468-1253(21)00215-6. [PMID: 34363763 DOI: 10.1016/S2468-1253(21)00215-6] [Reference Citation Analysis]
6 Yalchin M, Baker AM, Graham TA, Hart A. Predicting Colorectal Cancer Occurrence in IBD. Cancers (Basel) 2021;13:2908. [PMID: 34200768 DOI: 10.3390/cancers13122908] [Reference Citation Analysis]
7 Glissen Brown JR, Bilal M, Wang P, Berzin TM. Introducing computer-aided detection to the endoscopy suite. VideoGIE 2020;5:135-7. [PMID: 32258840 DOI: 10.1016/j.vgie.2020.01.002] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Wynants L, Smits LJM, Van Calster B. Demystifying AI in healthcare. BMJ 2020;370:m3505. [PMID: 32907834 DOI: 10.1136/bmj.m3505] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
9 Lui TKL, Hui CKY, Tsui VWM, Cheung KS, Ko MKL, Foo DCC, Mak LY, Yeung CK, Lui TH, Wong SY, Leung WK. New insights on missed colonic lesions during colonoscopy through artificial intelligence-assisted real-time detection (with video). Gastrointest Endosc. 2021;93:193-200.e1. [PMID: 32376335 DOI: 10.1016/j.gie.2020.04.066] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
10 Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J Gastroenterol 2021; 27(27): 4395-4412 [PMID: 34366612 DOI: 10.3748/wjg.v27.i27.4395] [Reference Citation Analysis]
11 Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14(1): 124-152 [DOI: 10.4251/wjgo.v14.i1.124] [Reference Citation Analysis]
12 Wang Y, He X, Nie H, Zhou J, Cao P, Ou C. Application of artificial intelligence to the diagnosis and therapy of colorectal cancer. Am J Cancer Res. 2020;10:3575-3598. [PMID: 33294256 DOI: 10.7150/thno.49168] [Cited by in Crossref: 10] [Cited by in F6Publishing: 14] [Article Influence: 5.0] [Reference Citation Analysis]
13 Li J, Lu J, Yan J, Tan Y, Liu D. Artificial intelligence can increase the detection rate of colorectal polyps and adenomas: a systematic review and meta-analysis. European Journal of Gastroenterology & Hepatology 2021;33:1041-8. [DOI: 10.1097/meg.0000000000001906] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
14 Hwang JH, Jamidar P, Kyanam Kabir Baig KR, Leung FW, Lightdale JR, Maranki JL, Okolo PI 3rd, Swanstrom LL, Chak A. GIE Editorial Board top 10 topics: advances in GI endoscopy in 2019. Gastrointest Endosc 2020;92:241-51. [PMID: 32470427 DOI: 10.1016/j.gie.2020.05.021] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Yang H, Hu B. Early gastrointestinal cancer: The application of artificial intelligence. Artif Intell Gastrointest Endosc 2021; 2(4): 185-197 [DOI: 10.37126/aige.v2.i4.185] [Reference Citation Analysis]
16 Gao L, Jiao T, Feng Q, Wang W. Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis. Osteoporos Int 2021;32:1279-86. [DOI: 10.1007/s00198-021-05887-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Keenan TDL, Clemons TE, Domalpally A, Elman MJ, Havilio M, Agrón E, Benyamini G, Chew EY. Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study. Ophthalmology 2021;128:100-9. [PMID: 32598950 DOI: 10.1016/j.ophtha.2020.06.038] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
18 Bilal M, Brown JRG, Berzin TM. Incorporating standardised reporting guidelines in clinical trials of artificial intelligence in gastrointestinal endoscopy. Lancet Gastroenterol Hepatol 2020;5:962-4. [PMID: 32918871 DOI: 10.1016/S2468-1253(20)30289-2] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
19 Mathis-Ullrich F, Scheikl PM. [Robots in the operating room-(co)operation during surgery]. Gastroenterologe 2020;:1-8. [PMID: 33362879 DOI: 10.1007/s11377-020-00496-x] [Reference Citation Analysis]
20 Repici A, Hassan C. Artificial intelligence for colonoscopy: the new Silk Road. Endoscopy 2021;53:285-7. [PMID: 33631828 DOI: 10.1055/a-1367-1979] [Reference Citation Analysis]
21 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]
22 Keswani RN, Byrd D, Garcia Vicente F, Heller JA, Klug M, Mazumder NR, Wood J, Yang AD, Etemadi M. Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning. Endosc Int Open 2021;9:E233-8. [PMID: 33553586 DOI: 10.1055/a-1326-1289] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Hassan C, Bhandari P, Antonelli G, Repici A. Artificial intelligence for non-polypoid colorectal neoplasms. Dig Endosc. 2020;Online ahead of print. [PMID: 32767704 DOI: 10.1111/den.13807] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
24 Topol EJ. Welcoming new guidelines for AI clinical research. Nat Med 2020;26:1318-20. [DOI: 10.1038/s41591-020-1042-x] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 6.5] [Reference Citation Analysis]
25 Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health 2020;2:e537-48. [PMID: 33328048 DOI: 10.1016/S2589-7500(20)30218-1] [Cited by in Crossref: 15] [Cited by in F6Publishing: 6] [Article Influence: 7.5] [Reference Citation Analysis]
26 Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, Antonelli G, Yu H, Areia M, Dinis-Ribeiro M, Bhandari P, Sharma P, Rex DK, Rösch T, Wallace M, Repici A. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc 2021;93:77-85.e6. [PMID: 32598963 DOI: 10.1016/j.gie.2020.06.059] [Cited by in Crossref: 37] [Cited by in F6Publishing: 26] [Article Influence: 18.5] [Reference Citation Analysis]
27 Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, Lei L, Li L, Guo Z, Lei S, Xiong F, Wang H, Song Y, Pan Y, Zhou G. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. The Lancet Gastroenterology & Hepatology 2020;5:343-51. [DOI: 10.1016/s2468-1253(19)30411-x] [Cited by in Crossref: 96] [Cited by in F6Publishing: 43] [Article Influence: 48.0] [Reference Citation Analysis]
28 Misawa M, Kudo SE, Mori Y, Maeda Y, Ogawa Y, Ichimasa K, Kudo T, Wakamura K, Hayashi T, Miyachi H, Baba T, Ishida F, Itoh H, Oda M, Mori K. Current status and future perspective on artificial intelligence for lower endoscopy. Dig Endosc 2021;33:273-84. [PMID: 32969051 DOI: 10.1111/den.13847] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
29 Perrod G, Rahmi G, Cellier C. Colorectal cancer screening in Lynch syndrome: Indication, techniques and future perspectives. Dig Endosc 2021;33:520-8. [PMID: 32314431 DOI: 10.1111/den.13702] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
30 Puri P, Comfere N, Drage LA, Shamim H, Bezalel SA, Pittelkow MR, Davis MDP, Wang M, Mangold AR, Tollefson MM, Lehman JS, Meves A, Yiannias JA, Otley CC, Carter RE, Sokumbi O, Hall MR, Bridges AG, Murphree DH. Deep learning for dermatologists: Part II. Current applications. J Am Acad Dermatol 2020:S0190-9622(20)30918-X. [PMID: 32428608 DOI: 10.1016/j.jaad.2020.05.053] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
31 Vercauteren T, Unberath M, Padoy N, Navab N. CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions. Proc IEEE Inst Electr Electron Eng. 2020;108:198-214. [PMID: 31920208 DOI: 10.1109/jproc.2019.2946993] [Cited by in Crossref: 32] [Cited by in F6Publishing: 11] [Article Influence: 10.7] [Reference Citation Analysis]
32 Lafeuille P, Lambin T, Yzet C, Latif EH, Ordoqui N, Rivory J, Pioche M. Flat colorectal sessile serrated polyp: an example of what artificial intelligence does not easily detect. Endoscopy 2021. [PMID: 33979854 DOI: 10.1055/a-1486-6220] [Reference Citation Analysis]
33 Ahmad OF. Deep learning for colorectal polyp detection: time for clinical implementation? Lancet Gastroenterol Hepatol 2020;5:330-1. [PMID: 31981521 DOI: 10.1016/S2468-1253(19)30431-5] [Reference Citation Analysis]
34 Lovejoy CA, Alqahtani SA. AI in colonoscopy and beyond: On the cusp of clinical implementation? United European Gastroenterol J 2021;9:525-6. [PMID: 33960666 DOI: 10.1002/ueg2.12076] [Reference Citation Analysis]
35 Glissen Brown JR, Mansour NM, Wang P, Chuchuca MA, Minchenberg SB, Chandnani M, Liu L, Gross SA, Sengupta N, Berzin TM. Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial). Clin Gastroenterol Hepatol 2021:S1542-3565(21)00973-3. [PMID: 34530161 DOI: 10.1016/j.cgh.2021.09.009] [Reference Citation Analysis]
36 Luo Y, Zhang Y, Liu M, Lai Y, Liu P, Wang Z, Xing T, Huang Y, Li Y, Li A, Wang Y, Luo X, Liu S, Han Z. Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study. J Gastrointest Surg 2021;25:2011-8. [PMID: 32968933 DOI: 10.1007/s11605-020-04802-4] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
37 Kawanishi K, Maekita T, Ikeda Y, Furotani M, Tsuboi S, Kanno T, Niwa T, Nagaoka T, Tabata Y, Hatamaru K, Iguchi M, Kitano M. Oral indigo carmine for the detection of colon adenoma. Scand J Gastroenterol 2021;56:351-5. [PMID: 33378628 DOI: 10.1080/00365521.2020.1867897] [Reference Citation Analysis]
38 Ashat M, Klair JS, Singh D, Murali AR, Krishnamoorthi R. Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis.Endosc Int Open. 2021;9:E513-E521. [PMID: 33816771 DOI: 10.1055/a-1341-0457] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
39 Yamada M, Saito Y, Yamada S, Kondo H, Hamamoto R. Detection of flat colorectal neoplasia by artificial intelligence: A systematic review. Best Pract Res Clin Gastroenterol 2021;52-53:101745. [PMID: 34172250 DOI: 10.1016/j.bpg.2021.101745] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
40 Su R, Liu J, Wu B, Xie Y, Zhang Y, Zhang W, Zhang Y, Wan M, Tian Z, Hu Y. Accurate measurement of colorectal polyps using computer-aided analysis. Eur J Gastroenterol Hepatol 2021;33:701-8. [PMID: 33787542 DOI: 10.1097/MEG.0000000000002162] [Reference Citation Analysis]
41 Tang Y, Anandasabapathy S, Richards-Kortum R. Advances in optical gastrointestinal endoscopy: a technical review. Mol Oncol 2021;15:2580-99. [PMID: 32915503 DOI: 10.1002/1878-0261.12792] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
42 Chahal D, Byrne MF. A primer on artificial intelligence and its application to endoscopy. Gastrointest Endosc 2020;92:813-820.e4. [PMID: 32387497 DOI: 10.1016/j.gie.2020.04.074] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
43 Hoerter N, Gross SA, Liang PS. Artificial Intelligence and Polyp Detection. Curr Treat Options Gastroenterol. 2020;. [PMID: 31960282 DOI: 10.1007/s11938-020-00274-2] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
44 He YS, Su JR, Li Z, Zuo XL, Li YQ. Application of artificial intelligence in gastrointestinal endoscopy. J Dig Dis. 2019;20:623-630. [PMID: 31639272 DOI: 10.1111/1751-2980.12827] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
45 Hoogenboom SA, Bagci U, Wallace MB. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when? Techniques and Innovations in Gastrointestinal Endoscopy 2020;22:42-7. [DOI: 10.1016/j.tgie.2019.150634] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 3.5] [Reference Citation Analysis]
46 Rex DK. The Case for High-Quality Colonoscopy Remaining a Premier Colorectal Cancer Screening Strategy in the United States. Gastrointest Endosc Clin N Am 2020;30:527-40. [PMID: 32439086 DOI: 10.1016/j.giec.2020.02.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
47 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]
48 Rees CJ, Koo S. Artificial intelligence — upping the game in gastrointestinal endoscopy? Nat Rev Gastroenterol Hepatol 2019;16:584-5. [DOI: 10.1038/s41575-019-0178-y] [Cited by in Crossref: 13] [Cited by in F6Publishing: 9] [Article Influence: 4.3] [Reference Citation Analysis]
49 Attardo S, Chandrasekar VT, Spadaccini M, Maselli R, Patel HK, Desai M, Capogreco A, Badalamenti M, Galtieri PA, Pellegatta G, Fugazza A, Carrara S, Anderloni A, Occhipinti P, Hassan C, Sharma P, Repici A. Artificial intelligence technologies for the detection of colorectal lesions: The future is now. World J Gastroenterol 2020; 26(37): 5606-5616 [PMID: 33088155 DOI: 10.3748/wjg.v26.i37.5606] [Cited by in CrossRef: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
50 Kazmierska J, Hope A, Spezi E, Beddar S, Nailon WH, Osong B, Ankolekar A, Choudhury A, Dekker A, Redalen KR, Traverso A. From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community. Radiother Oncol 2020;153:43-54. [PMID: 33065188 DOI: 10.1016/j.radonc.2020.09.054] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
51 Beyna T. May the force be with you: will artificial intelligence take over traditional endoscopy? Endoscopy 2021;53:499-500. [PMID: 33887780 DOI: 10.1055/a-1313-7499] [Reference Citation Analysis]
52 Pieszko K, Hiczkiewicz J, Budzianowski J, Musielak B, Hiczkiewicz D, Faron W, Rzeźniczak J, Burchardt P. Clinical applications of artificial intelligence in cardiology on the verge of the decade. Cardiol J 2021;28:460-72. [PMID: 32648252 DOI: 10.5603/CJ.a2020.0093] [Reference Citation Analysis]
53 Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, Ferrara E, Spadaccini M, Alkandari A, Fugazza A, Anderloni A, Galtieri PA, Pellegatta G, Carrara S, Di Leo M, Craviotto V, Lamonaca L, Lorenzetti R, Andrealli A, Antonelli G, Wallace M, Sharma P, Rosch T, Hassan C. Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial. Gastroenterology. 2020;159:512-520.e7. [PMID: 32371116 DOI: 10.1053/j.gastro.2020.04.062] [Cited by in Crossref: 73] [Cited by in F6Publishing: 72] [Article Influence: 36.5] [Reference Citation Analysis]
54 Auger SD, Jacobs BM, Dobson R, Marshall CR, Noyce AJ. Big data, machine learning and artificial intelligence: a neurologist's guide. Pract Neurol 2020:practneurol-2020-002688. [PMID: 32994368 DOI: 10.1136/practneurol-2020-002688] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
55 Ito-Masui A, Kawamoto E, Sakamoto R, Yu H, Sano A, Motomura E, Tanii H, Sakano S, Esumi R, Imai H, Shimaoka M. Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study. JMIR Res Protoc 2021;10:e24799. [PMID: 33626497 DOI: 10.2196/24799] [Reference Citation Analysis]
56 Yao Y, Gou S, Tian R, Zhang X, He S. Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network. Biomed Res Int. 2021;2021:6683931. [PMID: 33542924 DOI: 10.1155/2021/6683931] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
57 Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell 2021;39:916-27. [PMID: 33930310 DOI: 10.1016/j.ccell.2021.04.002] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
58 Goh JHL, Lim ZW, Fang X, Anees A, Nusinovici S, Rim TH, Cheng C, Tham Y. Artificial Intelligence for Cataract Detection and Management. Asia-Pacific Journal of Ophthalmology 2020;9:88-95. [DOI: 10.1097/01.apo.0000656988.16221.04] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
59 Park SH, Choi J, Byeon JS. Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence. Korean J Radiol 2021;22:442-53. [PMID: 33629545 DOI: 10.3348/kjr.2021.0048] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
60 Almadi MA, Ho KY. Artificial inelegance in endoscopy: An updated auricle of Delphi! Saudi J Gastroenterol 2020;26:1-3. [PMID: 32098934 DOI: 10.4103/sjg.SJG_636_19] [Reference Citation Analysis]
61 Fonollà R, E. W. van der Zander Q, Schreuder RM, Masclee AAM, Schoon EJ, van der Sommen F, de With PHN. A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities. Applied Sciences 2020;10:5040. [DOI: 10.3390/app10155040] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
62 Kim JH, Nam SJ, Park SC. Usefulness of artificial intelligence in gastric neoplasms. World J Gastroenterol 2021; 27(24): 3543-3555 [PMID: 34239268 DOI: 10.3748/wjg.v27.i24.3543] [Reference Citation Analysis]
63 Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. Curr Oncol 2021;28:1581-607. [PMID: 33922402 DOI: 10.3390/curroncol28030149] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
64 Ebigbo A, Palm C, Probst A, Mendel R, Manzeneder J, Prinz F, de Souza LA, Papa JP, Siersema P, Messmann H. A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology. Endosc Int Open. 2019;7:E1616-E1623. [PMID: 31788542 DOI: 10.1055/a-1010-5705] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 5.3] [Reference Citation Analysis]
65 Haak HE, Gao X, Maas M, Waktola S, Benson S, Beets-Tan RGH, Beets GL, van Leerdam M, Melenhorst J. The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiation. Surg Endosc 2021. [PMID: 34642794 DOI: 10.1007/s00464-021-08685-7] [Reference Citation Analysis]
66 Mori Y, Kudo SE, Misawa M. Can artificial intelligence standardise colonoscopy quality? Lancet Gastroenterol Hepatol 2020;5:331-2. [PMID: 31981520 DOI: 10.1016/S2468-1253(19)30407-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
67 Kanth P, Inadomi JM. Screening and prevention of colorectal cancer. BMJ 2021;374:n1855. [PMID: 34526356 DOI: 10.1136/bmj.n1855] [Reference Citation Analysis]
68 Mori Y, Bretthauer M, Kalager M. Hopes and Hypes for Artificial Intelligence in Colorectal Cancer Screening. Gastroenterology 2021;161:774-7. [PMID: 33989659 DOI: 10.1053/j.gastro.2021.04.078] [Reference Citation Analysis]
69 Oakden-Rayner L, Dunnmon J, Carneiro G, Ré C. Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging. Proc ACM Conf Health Inference Learn (2020) 2020;2020:151-9. [PMID: 33196064 DOI: 10.1145/3368555.3384468] [Cited by in Crossref: 38] [Cited by in F6Publishing: 20] [Article Influence: 19.0] [Reference Citation Analysis]
70 Aguilera-Chuchuca MJ, Sánchez-Luna SA, González Suárez B, Ernest-Suárez K, Gelrud A, Berzin TM. The emerging role of artificial intelligence in gastrointestinal endoscopy: A review. Gastroenterol Hepatol 2021:S0210-5705(21)00309-5. [PMID: 34793895 DOI: 10.1016/j.gastrohep.2021.11.004] [Reference Citation Analysis]
71 Leenhardt R, Fernandez-Urien Sainz I, Rondonotti E, Toth E, Van de Bruaene C, Baltes P, Rosa BJ, Triantafyllou K, Histace A, Koulaouzidis A, Dray X, On Behalf Of The I-Care Group. PEACE: Perception and Expectations toward Artificial Intelligence in Capsule Endoscopy. J Clin Med 2021;10:5708. [PMID: 34884410 DOI: 10.3390/jcm10235708] [Reference Citation Analysis]
72 Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ;  SPIRIT-AI and CONSORT-AI Working Group;  SPIRIT-AI and CONSORT-AI Steering Group;  SPIRIT-AI and CONSORT-AI Consensus Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26:1351-1363. [PMID: 32908284 DOI: 10.1038/s41591-020-1037-7] [Cited by in Crossref: 57] [Cited by in F6Publishing: 50] [Article Influence: 28.5] [Reference Citation Analysis]
73 Akshintala VS, Khashab MA. Artificial intelligence in pancreaticobiliary endoscopy. J Gastroenterol Hepatol. 2021;36:25-30. [PMID: 33448514 DOI: 10.1111/jgh.15343] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
74 Jia Y, Lawton T, Burden J, McDermid J, Habli I. Safety-driven design of machine learning for sepsis treatment. J Biomed Inform 2021;117:103762. [PMID: 33798716 DOI: 10.1016/j.jbi.2021.103762] [Reference Citation Analysis]
75 García-Peraza-Herrera LC, Everson M, Lovat L, Wang HP, Wang WL, Haidry R, Stoyanov D, Ourselin S, Vercauteren T. Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology. Int J Comput Assist Radiol Surg 2020;15:651-9. [PMID: 32166574 DOI: 10.1007/s11548-020-02127-w] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
76 Groot OQ, Bongers MER, Ogink PT, Senders JT, Karhade AV, Bramer JAM, Verlaan JJ, Schwab JH. Does Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A Systematic Review. Clin Orthop Relat Res 2020;478:2751-64. [PMID: 32740477 DOI: 10.1097/CORR.0000000000001360] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]
77 Alloro R, Sinagra E. Artificial intelligence and colorectal cancer: How far can you go? Artif Intell Cancer 2021; 2(2): 7-11 [DOI: 10.35713/aic.v2.i2.7] [Reference Citation Analysis]
78 Drukker L, Noble JA, Papageorghiou AT. Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology. Ultrasound Obstet Gynecol 2020;56:498-505. [PMID: 32530098 DOI: 10.1002/uog.22122] [Cited by in Crossref: 25] [Cited by in F6Publishing: 19] [Article Influence: 12.5] [Reference Citation Analysis]
79 Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020;26:1364-74. [PMID: 32908283 DOI: 10.1038/s41591-020-1034-x] [Cited by in Crossref: 85] [Cited by in F6Publishing: 71] [Article Influence: 42.5] [Reference Citation Analysis]
80 Berzin TM, Parasa S, Wallace MB, Gross SA, Repici A, Sharma P. Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Gastrointest Endosc. 2020;92:951-959. [PMID: 32565188 DOI: 10.1016/j.gie.2020.06.035] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
81 McNeil MB, Gross SA. Siri here, cecum reached, but please wash that fold: Will artificial intelligence improve gastroenterology? Gastrointest Endosc. 2020;91:425-427. [PMID: 32036947 DOI: 10.1016/j.gie.2019.10.027] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
82 Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol 2021; 27(29): 4802-4817 [PMID: 34447227 DOI: 10.3748/wjg.v27.i29.4802] [Reference Citation Analysis]
83 Misawa M, Kudo SE, Mori Y, Hotta K, Ohtsuka K, Matsuda T, Saito S, Kudo T, Baba T, Ishida F, Itoh H, Oda M, Mori K. Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Gastrointest Endosc 2021;93:960-967.e3. [PMID: 32745531 DOI: 10.1016/j.gie.2020.07.060] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 8.5] [Reference Citation Analysis]
84 Parasa S, Wallace M, Bagci U, Antonino M, Berzin T, Byrne M, Celik H, Farahani K, Golding M, Gross S, Jamali V, Mendonca P, Mori Y, Ninh A, Repici A, Rex D, Skrinak K, Thakkar SJ, van Hooft JE, Vargo J, Yu H, Xu Z, Sharma P. Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit. Gastrointest Endosc 2020; 92: 938-945. e1. [PMID: 32343978 DOI: 10.1016/j.gie.2020.04.044] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
85 Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Koda H, Miyano A, Fumihara D. Current status of artificial intelligence analysis for endoscopic ultrasonography. Dig Endosc. 2021;33:298-305. [PMID: 33098123 DOI: 10.1111/den.13880] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
86 Fujii M, Isomoto H. Next generation of endoscopy: Harmony with artificial intelligence and robotic-assisted devices. Dig Endosc 2020;32:526-8. [PMID: 32045036 DOI: 10.1111/den.13649] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
87 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]
88 Yoo BS, D'Souza SM, Houston K, Patel A, Lau J, Elmahdi A, Parekh PJ, Johnson D. Artificial intelligence and colonoscopy − enhancements and improvements. Artif Intell Gastrointest Endosc 2021; 2(4): 157-167 [DOI: 10.37126/aige.v2.i4.157] [Reference Citation Analysis]
89 Dominitz JA, Ko CW. Managing the Measurement of Colonoscopy Quality. Am J Gastroenterol 2019;114:1199-201. [PMID: 31241546 DOI: 10.14309/ajg.0000000000000307] [Reference Citation Analysis]
90 Podlasek J, Heesch M, Podlasek R, Kilisiński W, Filip R. Real-time deep learning-based colorectal polyp localization on clinical video footage achievable with a wide array of hardware configurations. Endosc Int Open 2021;9:E741-8. [PMID: 33937516 DOI: 10.1055/a-1388-6735] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
91 Sharma P, Pante A, Gross SA. Artificial intelligence in endoscopy. Gastrointestinal Endoscopy 2020;91:925-31. [DOI: 10.1016/j.gie.2019.12.018] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
92 Repici A, Spadaccini M, Antonelli G, Correale L, Maselli R, Galtieri PA, Pellegatta G, Capogreco A, Milluzzo SM, Lollo G, Di Paolo D, Badalamenti M, Ferrara E, Fugazza A, Carrara S, Anderloni A, Rondonotti E, Amato A, De Gottardi A, Spada C, Radaelli F, Savevski V, Wallace MB, Sharma P, Rösch T, Hassan C. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut 2021:gutjnl-2021-324471. [PMID: 34187845 DOI: 10.1136/gutjnl-2021-324471] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
93 Xu L, He X, Zhou J, Zhang J, Mao X, Ye G, Chen Q, Xu F, Sang J, Wang J, Ding Y, Li Y, Yu C. Artificial intelligence-assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection. Cancer Med 2021;10:7184-93. [PMID: 34477306 DOI: 10.1002/cam4.4261] [Reference Citation Analysis]
94 Nogueira-rodríguez A, Domínguez-carbajales R, López-fernández H, Iglesias Á, Cubiella J, Fdez-riverola F, Reboiro-jato M, Glez-peña D. Deep Neural Networks approaches for detecting and classifying colorectal polyps. Neurocomputing 2021;423:721-34. [DOI: 10.1016/j.neucom.2020.02.123] [Cited by in Crossref: 9] [Cited by in F6Publishing: 2] [Article Influence: 9.0] [Reference Citation Analysis]
95 Luo X, Wang J, Han Z, Yu Y, Chen Z, Huang F, Xu Y, Cai J, Zhang Q, Qiao W, Ng IC, Tan RT, Liu S, Yu H. Artificial intelligence-enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth. Gastrointest Endosc 2021;94:627-638.e1. [PMID: 33852902 DOI: 10.1016/j.gie.2021.03.936] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
96 Yang YJ, Cho BJ, Lee MJ, Kim JH, Lim H, Bang CS, Jeong HM, Hong JT, Baik GH. Automated Classification of Colorectal Neoplasms in White-Light Colonoscopy Images via Deep Learning. J Clin Med. 2020;9. [PMID: 32456309 DOI: 10.3390/jcm9051593] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 5.5] [Reference Citation Analysis]
97 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]
98 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]
99 Garofalo M, Piccoli L, Romeo M, Barzago MM, Ravasio S, Foglierini M, Matkovic M, Sgrignani J, De Gasparo R, Prunotto M, Varani L, Diomede L, Michielin O, Lanzavecchia A, Cavalli A. Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity. Nat Commun 2021;12:3532. [PMID: 34112780 DOI: 10.1038/s41467-021-23880-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
100 Berzin TM, Topol EJ. Adding artificial intelligence to gastrointestinal endoscopy. Lancet 2020;395:485. [PMID: 32061286 DOI: 10.1016/S0140-6736(20)30294-4] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
101 Wang P, Liu P, Glissen Brown JR, Berzin TM, Zhou G, Lei S, Liu X, Li L, Xiao X. Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study. Gastroenterology. 2020;159:1252-1261.e5. [PMID: 32562721 DOI: 10.1053/j.gastro.2020.06.023] [Cited by in Crossref: 28] [Cited by in F6Publishing: 25] [Article Influence: 14.0] [Reference Citation Analysis]
102 Cao M, Li H, Sun D, He S, Yu Y, Li J, Chen H, Shi J, Ren J, Li N, Chen W. Cancer screening in China: The current status, challenges, and suggestions. Cancer Lett 2021;506:120-7. [PMID: 33684533 DOI: 10.1016/j.canlet.2021.02.017] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
103 Sinagra E, Badalamenti M, Maida M, Spadaccini M, Maselli R, Rossi F, Conoscenti G, Raimondo D, Pallio S, Repici A, Anderloni A. Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped. World J Gastroenterol 2020; 26(39): 5911-5918 [PMID: 33132644 DOI: 10.3748/wjg.v26.i39.5911] [Cited by in CrossRef: 8] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
104 Tang CP, Shao PP, Hsieh YH, Leung FW. A review of water exchange and artificial intelligence in improving adenoma detection. Tzu Chi Med J 2021;33:108-14. [PMID: 33912406 DOI: 10.4103/tcmj.tcmj_88_20] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
105 Pannala R, Krishnan K, Melson J, Parsi MA, Schulman AR, Sullivan S, Trikudanathan G, Trindade AJ, Watson RR, Maple JT, Lichtenstein DR. Artificial intelligence in gastrointestinal endoscopy. VideoGIE. 2020;5:598-613. [PMID: 33319126 DOI: 10.1016/j.vgie.2020.08.013] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
106 Li X, Kulkarni AS, Liu X, Gao W, Huang L, Hu Z, Qian K. Metal‐Organic Framework Hybrids Aid Metabolic Profiling for Colorectal Cancer. Small Methods 2021;5:2001001. [DOI: 10.1002/smtd.202001001] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
107 Bogie RMM, Winkens B, Retra SJJ, le Clercq CMC, Bouwens MW, Rondagh EJA, Chang LC, de Ridder R, Hoge C, Straathof JW, Goudkade D, Sanduleanu-Dascalescu S, Masclee AAM. Metachronous neoplasms in patients with laterally spreading tumours during surveillance. United European Gastroenterol J 2021;9:378-87. [PMID: 33245025 DOI: 10.1177/2050640620965317] [Reference Citation Analysis]
108 Li T, Glissen Brown JR, Tsourides K, Mahmud N, Cohen JM, Berzin TM. Training a computer-aided polyp detection system to detect sessile serrated adenomas using public domain colonoscopy videos. Endosc Int Open 2020;8:E1448-54. [PMID: 33043112 DOI: 10.1055/a-1229-3927] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
109 Phillips F, Beg S. Video capsule endoscopy: pushing the boundaries with software technology. Transl Gastroenterol Hepatol 2021;6:17. [PMID: 33409411 DOI: 10.21037/tgh.2020.02.01] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
110 Lui TKL, Leung WK. Is artificial intelligence the final answer to missed polyps in colonoscopy? World J Gastroenterol 2020; 26(35): 5248-5255 [PMID: 32994685 DOI: 10.3748/wjg.v26.i35.5248] [Cited by in CrossRef: 1] [Article Influence: 0.5] [Reference Citation Analysis]
111 Keenan TDL, Chakravarthy U, Loewenstein A, Chew EY, Schmidt-Erfurth U. Automated Quantitative Assessment of Retinal Fluid Volumes as Important Biomarkers in Neovascular Age-Related Macular Degeneration. Am J Ophthalmol 2021;224:267-81. [PMID: 33359681 DOI: 10.1016/j.ajo.2020.12.012] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
112 Shung DL. Advancing care for acute gastrointestinal bleeding using artificial intelligence. J Gastroenterol Hepatol 2021;36:273-8. [PMID: 33624892 DOI: 10.1111/jgh.15372] [Reference Citation Analysis]
113 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]
114 Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, Topol EJ, Ioannidis JPA, Collins GS, Maruthappu M. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 2020;368:m689. [PMID: 32213531 DOI: 10.1136/bmj.m689] [Cited by in Crossref: 143] [Cited by in F6Publishing: 120] [Article Influence: 71.5] [Reference Citation Analysis]
115 Hann A, Troya J, Fitting D. Current status and limitations of artificial intelligence in colonoscopy. United European Gastroenterol J 2021;9:527-33. [PMID: 34617420 DOI: 10.1002/ueg2.12108] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
116 Markarian E, Fung BM, Girotra M, Tabibian JH. Large polyps: Pearls for the referring and receiving endoscopist. World J Gastrointest Endosc 2021; 13(12): 638-648 [DOI: 10.4253/wjge.v13.i12.638] [Reference Citation Analysis]
117 Chao WL, Manickavasagan H, Krishna SG. Application of Artificial Intelligence in the Detection and Differentiation of Colon Polyps: A Technical Review for Physicians. Diagnostics (Basel). 2019;9. [PMID: 31434208 DOI: 10.3390/diagnostics9030099] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 4.7] [Reference Citation Analysis]
118 Zhang Y, Yu H, Dong R, Ji X, Li F. Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis. Biomed Res Int 2021;2021:5592472. [PMID: 33763475 DOI: 10.1155/2021/5592472] [Reference Citation Analysis]
119 Milluzzo SM, Cesaro P, Grazioli LM, Olivari N, Spada C. Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective. Clin Endosc 2021;54:329-39. [PMID: 33434961 DOI: 10.5946/ce.2020.082] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
120 Zeng T, Yu X, Chen Z. Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review. J Gastroenterol Hepatol 2021;36:832-40. [PMID: 33880762 DOI: 10.1111/jgh.15503] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
121 Okamoto Y, Yoshida S, Izakura S, Katayama D, Michida R, Koide T, Tamaki T, Kamigaichi Y, Tamari H, Shimohara Y, Nishimura T, Inagaki K, Tanaka H, Yamashita K, Sumimoto K, Oka S, Tanaka S. Development of multi-class computer-aided diagnostic systems using the NICE/JNET classifications for colorectal lesions. J Gastroenterol Hepatol 2021. [PMID: 34478167 DOI: 10.1111/jgh.15682] [Reference Citation Analysis]
122 Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health 2020;2:e549-60. [PMID: 33328049 DOI: 10.1016/S2589-7500(20)30219-3] [Cited by in Crossref: 22] [Cited by in F6Publishing: 5] [Article Influence: 11.0] [Reference Citation Analysis]
123 Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021; 27(47): 8103-8122 [DOI: 10.3748/wjg.v27.i47.8103] [Reference Citation Analysis]
124 Mujtaba S, Chawla S, Massaad JF. Diagnosis and Management of Non-Variceal Gastrointestinal Hemorrhage: A Review of Current Guidelines and Future Perspectives. J Clin Med. 2020;9:402. [PMID: 32024301 DOI: 10.3390/jcm9020402] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
125 Shen L, Kann BH, Taylor RA, Shung DL. The Clinician's Guide to the Machine Learning Galaxy. Front Physiol 2021;12:658583. [PMID: 33889088 DOI: 10.3389/fphys.2021.658583] [Reference Citation Analysis]
126 Kuang M, Hu HT, Li W, Chen SL, Lu XZ. Articles That Use Artificial Intelligence for Ultrasound: A Reader's Guide. Front Oncol 2021;11:631813. [PMID: 34178622 DOI: 10.3389/fonc.2021.631813] [Reference Citation Analysis]
127 Jin HY, Zhang M, Hu B. Techniques to integrate artificial intelligence systems with medical information in gastroenterology. Artif Intell Gastrointest Endosc 2020; 1(1): 19-27 [DOI: 10.37126/aige.v1.i1.19] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
128 Lee JY, Koh M, Lee JH. Latest Generation High-Definition Colonoscopy Increases Adenoma Detection Rate by Trainee Endoscopists. Dig Dis Sci 2021;66:2756-62. [PMID: 32808142 DOI: 10.1007/s10620-020-06543-5] [Reference Citation Analysis]
129 Abadir AP, Ali MF, Karnes W, Samarasena JB. Artificial Intelligence in Gastrointestinal Endoscopy. Clin Endosc. 2020;53:132-141. [PMID: 32252506 DOI: 10.5946/ce.2020.038] [Cited by in Crossref: 24] [Cited by in F6Publishing: 19] [Article Influence: 12.0] [Reference Citation Analysis]
130 Zhu XW, Yan J, He YL, Liu G, Li X. Application of deep learning based artificial intelligence technology in identification of colorectal polyps. Shijie Huaren Xiaohua Zazhi 2021; 29(20): 1201-1206 [DOI: 10.11569/wcjd.v29.i20.1201] [Reference Citation Analysis]
131 Trasolini R, Byrne MF. Artificial intelligence and deep learning for small bowel capsule endoscopy. Dig Endosc 2021;33:290-7. [PMID: 33211357 DOI: 10.1111/den.13896] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
132 Wang P, Berzin TM, Glissen Brown JR. Reply. Gastroenterology 2021;160:2212-3. [PMID: 33516702 DOI: 10.1053/j.gastro.2021.01.217] [Reference Citation Analysis]
133 Tang CP, Chen KH, Lin TL. Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques. Sensors (Basel) 2021;21:5315. [PMID: 34450756 DOI: 10.3390/s21165315] [Reference Citation Analysis]
134 Nagao S, Tsuji Y, Sakaguchi Y, Takahashi Y, Minatsuki C, Niimi K, Yamashita H, Yamamichi N, Seto Y, Tada T, Koike K. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging. Gastrointestinal Endoscopy 2020;92:866-873.e1. [DOI: 10.1016/j.gie.2020.06.047] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 9.0] [Reference Citation Analysis]
135 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]
136 Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK;  SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370:m3164. [PMID: 32909959 DOI: 10.1136/bmj.m3164] [Cited by in Crossref: 29] [Cited by in F6Publishing: 32] [Article Influence: 14.5] [Reference Citation Analysis]
137 Eelbode T, Sinonquel P, Maes F, Bisschops R. Pitfalls in training and validation of deep learning systems. Best Pract Res Clin Gastroenterol 2021;52-53:101712. [PMID: 34172245 DOI: 10.1016/j.bpg.2020.101712] [Reference Citation Analysis]
138 Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020;92:807-812. [PMID: 32565184 DOI: 10.1016/j.gie.2020.06.040] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 7.0] [Reference Citation Analysis]
139 Ortega-Morán JF, Azpeitia Á, Sánchez-Peralta LF, Bote-Curiel L, Pagador B, Cabezón V, Saratxaga CL, Sánchez-Margallo FM. Medical needs related to the endoscopic technology and colonoscopy for colorectal cancer diagnosis. BMC Cancer 2021;21:467. [PMID: 33902503 DOI: 10.1186/s12885-021-08190-z] [Reference Citation Analysis]
140 Aziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis. J Gastroenterol Hepatol. 2020;35:1676-1683. [PMID: 32267558 DOI: 10.1111/jgh.15070] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 7.5] [Reference Citation Analysis]
141 Nazarian S, Glover B, Ashrafian H, Darzi A, Teare J. Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-analysis. J Med Internet Res 2021;23:e27370. [PMID: 34259645 DOI: 10.2196/27370] [Reference Citation Analysis]
142 Madalinski M. Real-Time Computer-Aided Detection for colorectal neoplasia or only small polyps? JMIR Med Inform 2021. [PMID: 34571490 DOI: 10.2196/25328] [Reference Citation Analysis]
143 Shaukat A, Colucci D, Erisson L, Phillips S, Ng J, Iglesias JE, Saltzman JR, Somers S, Brugge W. Improvement in adenoma detection using a novel artificial intelligence-aided polyp detection device. Endosc Int Open 2021;9:E263-70. [PMID: 33553591 DOI: 10.1055/a-1321-1317] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
144 Zhao SB, Yang W, Wang SL, Pan P, Wang RD, Chang X, Sun ZQ, Fu XH, Shang H, Wu JR, Chen LZ, Chang J, Song P, Miao YL, He SX, Miao L, Jiang HQ, Wang W, Yang X, Dong YH, Lin H, Chen Y, Gao J, Meng QQ, Jin ZD, Li ZS, Bai Y. Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning. World J Gastroenterol 2021; 27(31): 5232-5246 [PMID: 34497447 DOI: 10.3748/wjg.v27.i31.5232] [Reference Citation Analysis]
145 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]
146 Ahmad OF, Mori Y, Misawa M, Kudo SE, Anderson JT, Bernal J, Berzin TM, Bisschops R, Byrne MF, Chen PJ, East JE, Eelbode T, Elson DS, Gurudu SR, Histace A, Karnes WE, Repici A, Singh R, Valdastri P, Wallace MB, Wang P, Stoyanov D, Lovat LB. Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method.Endoscopy. 2021;53:893-901. [PMID: 33167043 DOI: 10.1055/a-1306-7590] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
147 Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195. [PMID: 31665002 DOI: 10.1186/s12916-019-1426-2] [Cited by in Crossref: 209] [Cited by in F6Publishing: 159] [Article Influence: 69.7] [Reference Citation Analysis]
148 Mori Y, Neumann H, Misawa M, Kudo SE, Bretthauer M. Artificial intelligence in colonoscopy - Now on the market. What's next? J Gastroenterol Hepatol. 2021;36:7-11. [PMID: 33179322 DOI: 10.1111/jgh.15339] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 10.0] [Reference Citation Analysis]
149 Ding Z, Shi H, Zhang H, Meng L, Fan M, Han C, Zhang K, Ming F, Xie X, Liu H, Liu J, Lin R, Hou X. Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model. Gastroenterology 2019; 157: 1044-1054. e5. [PMID: 31251929 DOI: 10.1053/j.gastro.2019.06.025] [Cited by in Crossref: 81] [Cited by in F6Publishing: 72] [Article Influence: 27.0] [Reference Citation Analysis]
150 Sumiyama K, Futakuchi T, Kamba S, Matsui H, Tamai N. Artificial intelligence in endoscopy: Present and future perspectives. Digestive Endoscopy 2021;33:218-30. [DOI: 10.1111/den.13837] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
151 Yin J, Ngiam KY, Teo HH. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. J Med Internet Res 2021;23:e25759. [PMID: 33885365 DOI: 10.2196/25759] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
152 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]
153 Siontis GCM, Sweda R, Noseworthy PA, Friedman PA, Siontis KC, Patel CJ. Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials. BMJ Health Care Inform 2021;28:e100466. [PMID: 34969668 DOI: 10.1136/bmjhci-2021-100466] [Reference Citation Analysis]
154 Dougherty KE, Melkonian VJ, Montenegro GA. Artificial intelligence in polyp detection - where are we and where are we headed? Artif Intell Gastrointest Endosc 2021; 2(6): 211-219 [DOI: 10.37126/aige.v2.i6.211] [Reference Citation Analysis]
155 Shen P, Li WZ, Li JX, Pei ZC, Luo YX, Mu JB, Li W, Wang XM. Real-time use of a computer-aided system for polyp detection during colonoscopy, an ambispective study. J Dig Dis 2021;22:256-62. [PMID: 33742774 DOI: 10.1111/1751-2980.12985] [Reference Citation Analysis]
156 Bilal M, Glissen Brown JR, Berzin TM. Using Computer-Aided Polyp Detection During Colonoscopy. Am J Gastroenterol 2020;115:963-6. [PMID: 32618638 DOI: 10.14309/ajg.0000000000000646] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
157 Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020;158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Cited by in Crossref: 108] [Cited by in F6Publishing: 96] [Article Influence: 36.0] [Reference Citation Analysis]
158 Namikawa K, Hirasawa T, Yoshio T, Fujisaki J, Ozawa T, Ishihara S, Aoki T, Yamada A, Koike K, Suzuki H, Tada T. Utilizing artificial intelligence in endoscopy: a clinician's guide. Expert Rev Gastroenterol Hepatol. 2020;1-18. [PMID: 32500760 DOI: 10.1080/17474124.2020.1779058] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
159 Cantor DI, Cheruku HR, Westacott J, Shin JS, Mohamedali A, Ahn SB. Proteomic investigations into resistance in colorectal cancer. Expert Rev Proteomics 2020;17:49-65. [PMID: 31914823 DOI: 10.1080/14789450.2020.1713103] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
160 Lee J, Wallace MB. State of the Art: The impact of artificial intelligence in endoscopy 2020.Curr Gastroenterol Rep. 2021;23:7. [PMID: 33855659 DOI: 10.1007/s11894-021-00810-9] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
161 Wittenberg T, Raithel M. Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed? Visc Med. 2020;36:428-438. [PMID: 33447598 DOI: 10.1159/000512438] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
162 Antonelli G, Gkolfakis P, Tziatzios G, Papanikolaou IS, Triantafyllou K, Hassan C. Artificial intelligence-aided colonoscopy: Recent developments and future perspectives. World J Gastroenterol 2020; 26(47): 7436-7443 [PMID: 33384546 DOI: 10.3748/wjg.v26.i47.7436] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
163 Lee J, Bae JH, Chung SJ, Kang HY, Kang SJ, Kwak MS, Seo JY, Song JH, Yang SY, Yang JI, Lim SH, Yim JY, Lim JH, Chung GE, Jin EH, Choi JM, Han YM, Kim JS. Impact of comprehensive optical diagnosis training using Workgroup serrAted polypS and Polyposis classification on detection of adenoma and sessile serrated lesion. Dig Endosc 2021. [PMID: 34021513 DOI: 10.1111/den.14046] [Reference Citation Analysis]
164 Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021; 2(3): 79-88 [DOI: 10.37126/aige.v2.i3.79] [Reference Citation Analysis]
165 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]
166 Goyal H, Mann R, Gandhi Z, Perisetti A, Ali A, Aman Ali K, Sharma N, Saligram S, Tharian B, Inamdar S. Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer. J Clin Med 2020;9:E3313. [PMID: 33076511 DOI: 10.3390/jcm9103313] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
167 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]
168 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]
169 Deliwala SS, Hamid K, Barbarawi M, Lakshman H, Zayed Y, Kandel P, Malladi S, Singh A, Bachuwa G, Gurvits GE, Chawla S. Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis. Int J Colorectal Dis 2021. [PMID: 33934173 DOI: 10.1007/s00384-021-03929-3] [Reference Citation Analysis]
170 Kochhar GS, Carleton NM, Thakkar S. Assessing perspectives on artificial intelligence applications to gastroenterology. Gastrointest Endosc 2021;93:971-975.e2. [PMID: 33144237 DOI: 10.1016/j.gie.2020.10.029] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
171 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]
172 Lee JY, Lee JH. [Post-colonoscopy Colorectal Cancer: Causes and Prevention of Interval Colorectal Cancer]. Korean J Gastroenterol 2020;75:314-21. [PMID: 32581202 DOI: 10.4166/kjg.2020.75.6.314] [Reference Citation Analysis]
173 Fukuda H, Ishihara R, Kato Y, Matsunaga T, Nishida T, Yamada T, Ogiyama H, Horie M, Kinoshita K, Tada T. Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video). Gastrointest Endosc. 2020;92:848-855. [PMID: 32505685 DOI: 10.1016/j.gie.2020.05.043] [Cited by in Crossref: 18] [Cited by in F6Publishing: 21] [Article Influence: 9.0] [Reference Citation Analysis]
174 Qadir HA, Shin Y, Solhusvik J, Bergsland J, Aabakken L, Balasingham I. Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction. Med Image Anal 2021;68:101897. [PMID: 33260111 DOI: 10.1016/j.media.2020.101897] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
175 Lei S, Wang Z, Tu M, Liu P, Lei L, Xiao X, Zhou G, Liu X, Li L, Wang P. Adenoma detection rate is not influenced by the time of day in computer-aided detection colonoscopy. Medicine (Baltimore) 2020;99:e23685. [PMID: 33371110 DOI: 10.1097/MD.0000000000023685] [Reference Citation Analysis]
176 Ibrahim H, Liu X, Rivera SC, Moher D, Chan AW, Sydes MR, Calvert MJ, Denniston AK. Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines. Trials 2021;22:11. [PMID: 33407780 DOI: 10.1186/s13063-020-04951-6] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
177 Li JW, Ang TL. Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged? Artif Intell Gastrointest Endosc 2021; 2(2): 36-49 [DOI: 10.37126/aige.v2.i2.36] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
178 Zhou Q. Noninferiority Design in Trials Involving Artificial Intelligence Interventions: A Field Needs to Be Improved. Gastroenterology 2021;161:1072-3. [PMID: 33516700 DOI: 10.1053/j.gastro.2021.01.215] [Reference Citation Analysis]
179 Glissen Brown JR, Berzin TM. Adoption of New Technologies: Artificial Intelligence. Gastrointest Endosc Clin N Am 2021;31:743-58. [PMID: 34538413 DOI: 10.1016/j.giec.2021.05.010] [Reference Citation Analysis]
180 Walradt T, Glissen Brown JR, Alagappan M, Lerner HP, Berzin TM. Regulatory considerations for artificial intelligence technologies in GI endoscopy. Gastrointest Endosc. 2020;. [PMID: 32504697 DOI: 10.1016/j.gie.2020.05.040] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
181 Lee A, Tutticci N. Enhancing polyp detection: technological advances in colonoscopy imaging. Transl Gastroenterol Hepatol 2021;6:61. [PMID: 34805583 DOI: 10.21037/tgh.2020.02.05] [Reference Citation Analysis]
182 Wang T, Tsang T, Turshudzhyan A, Dacus H, Tadros M. Updates, Controversies, and Emerging Approaches in Colorectal Screening. Cureus 2021;13:e17844. [PMID: 34660050 DOI: 10.7759/cureus.17844] [Reference Citation Analysis]
183 Zhang Y, Zhang X, Wu Q, Gu C, Wang Z. Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. J Laparoendosc Adv Surg Tech A 2021. [PMID: 33524298 DOI: 10.1089/lap.2020.0777] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
184 Wu D, Chen S, Zhang Y, Zhang H, Wang Q, Li J, Fu Y, Wang S, Yang H, Du H, Zhu H, Pan H, Shen Z. Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition. J Pers Med 2021;11:1172. [PMID: 34834524 DOI: 10.3390/jpm11111172] [Reference Citation Analysis]
185 Glissen Brown JR, Waljee AK, Mori Y, Sharma P, Berzin TM. Charting a path forward for clinical research in artificial intelligence and gastroenterology. Dig Endosc 2021. [PMID: 33715244 DOI: 10.1111/den.13974] [Reference Citation Analysis]
186 Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021;41:1100-15. [PMID: 34613667 DOI: 10.1002/cac2.12215] [Reference Citation Analysis]
187 Syed S, Stidham RW. Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease. Inflamm Bowel Dis. 2020;26:1490-1497. [PMID: 32869844 DOI: 10.1093/ibd/izaa211] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
188 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]
189 Ozawa T, Ishihara S, Fujishiro M, Kumagai Y, Shichijo S, Tada T. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therap Adv Gastroenterol. 2020;13:1756284820910659. [PMID: 32231710 DOI: 10.1177/1756284820910659] [Cited by in Crossref: 25] [Cited by in F6Publishing: 23] [Article Influence: 12.5] [Reference Citation Analysis]
190 Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc 2021;14:26317745211014698. [PMID: 34263163 DOI: 10.1177/26317745211014698] [Reference Citation Analysis]
191 Karnes WE, Johnson DA, Berzin TM, Gross SA, Vargo JJ, Sharma P, Zachariah R, Samarasena JB, Anderson JC. A Polyp Worth Removing: A Paradigm for Measuring Colonoscopy Quality and Performance of Novel Technologies for Polyp Detection. J Clin Gastroenterol 2021;55:733-9. [PMID: 34334765 DOI: 10.1097/MCG.0000000000001594] [Reference Citation Analysis]
192 Chetcuti Zammit S, Sidhu R. Capsule endoscopy - Recent developments and future directions. Expert Rev Gastroenterol Hepatol 2021;15:127-37. [PMID: 33111600 DOI: 10.1080/17474124.2021.1840351] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
193 Glissen Brown JR, Berzin TM. EndoBRAIN-EYE and the SUN database: important steps forward for computer-aided polyp detection. Gastrointest Endosc 2021;93:968-70. [PMID: 33741095 DOI: 10.1016/j.gie.2020.09.016] [Reference Citation Analysis]
194 Zachariah R, Ninh A, Karnes W. Artificial intelligence for colon polyp detection: Why should we embrace this? Techniques and Innovations in Gastrointestinal Endoscopy 2020;22:48-51. [DOI: 10.1016/j.tgie.2019.150631] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
195 Kim KO, Kim EY. Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm. Gut Liver. 2021;15:346-353. [PMID: 32773386 DOI: 10.5009/gnl20186] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
196 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]
197 Parasher G, Wong M, Rawat M. Evolving role of artificial intelligence in gastrointestinal endoscopy. World J Gastroenterol 2020; 26(46): 7287-7298 [PMID: 33362384 DOI: 10.3748/wjg.v26.i46.7287] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
198 Su JR, Li Z, Shao XJ, Ji CR, Ji R, Zhou RC, Li GC, Liu GQ, He YS, Zuo XL, Li YQ. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc. 2020;91:415-424.e4. [PMID: 31454493 DOI: 10.1016/j.gie.2019.08.026] [Cited by in Crossref: 77] [Cited by in F6Publishing: 66] [Article Influence: 25.7] [Reference Citation Analysis]
199 Ilan Y. Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes. Front Digit Health 2020;2:569178. [DOI: 10.3389/fdgth.2020.569178] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
200 Yu Y, Yin YH, Min L, Zhu ST, Li P, Zhang ST. Challenge in the new era: Translational medicine in gastrointestinal endoscopy and early cancer. Chronic Dis Transl Med 2019;5:234-42. [PMID: 32055782 DOI: 10.1016/j.cdtm.2019.12.002] [Reference Citation Analysis]
201 Guo Z, Nemoto D, Zhu X, Li Q, Aizawa M, Utano K, Isohata N, Endo S, Kawarai Lefor A, Togashi K. Polyp detection algorithm can detect small polyps: Ex vivo reading test compared with endoscopists. Dig Endosc 2021;33:162-9. [PMID: 32173917 DOI: 10.1111/den.13670] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
202 Powell K, Prasad V. Old-fashioned Intelligence Will Always Be Needed in Medicine. Eur Urol Focus 2021;7:685-6. [PMID: 33824086 DOI: 10.1016/j.euf.2021.03.022] [Reference Citation Analysis]
203 Michopoulos S, Axiaris G, Baxevanis P, Stoupaki M, Gkagkari V, Leonidakis G, Zampeli E, Sotiropoulou M, Petraki K. Retroflexion, a costless endoscopic maneuver, increases adenoma detection rate in the ascending colon. Ann Gastroenterol 2021;34:53-60. [PMID: 33414622 DOI: 10.20524/aog.2020.0549] [Reference Citation Analysis]
204 Lui TK, Guo C, Leung WK. Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis. Gastrointestinal Endoscopy 2020;92:11-22.e6. [DOI: 10.1016/j.gie.2020.02.033] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 11.5] [Reference Citation Analysis]
205 Nakajima Y, Zhu X, Nemoto D, Li Q, Guo Z, Katsuki S, Hayashi Y, Utano K, Aizawa M, Takezawa T, Sagara Y, Shibukawa G, Yamamoto H, Lefor AK, Togashi K. Diagnostic performance of artificial intelligence to identify deeply invasive colorectal cancer on non-magnified plain endoscopic images. Endosc Int Open 2020;8:E1341-8. [PMID: 33015336 DOI: 10.1055/a-1220-6596] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
206 Le A, Salifu MO, McFarlane IM. Artificial Intelligence in Colorectal Polyp Detection and Characterization. Int J Clin Res Trials 2021;6:157. [PMID: 33884326 DOI: 10.15344/2456-8007/2021/157] [Reference Citation Analysis]
207 Drogan C, Kupfer SS. Colorectal Cancer Screening Recommendations and Outcomes in Lynch Syndrome. Gastrointest Endosc Clin N Am 2022;32:59-74. [PMID: 34798987 DOI: 10.1016/j.giec.2021.08.001] [Reference Citation Analysis]
208 Hardy NP, Mac Aonghusa P, Neary PM, Cahill RA. Intraprocedural Artificial Intelligence for Colorectal Cancer Detection and Characterisation in Endoscopy and Laparoscopy. Surg Innov 2021;:1553350621997761. [PMID: 33634722 DOI: 10.1177/1553350621997761] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
209 Hsieh YH, Tang CP, Tseng CW, Lin TL, Leung FW. Computer-Aided Detection False Positives in Colonoscopy. Diagnostics (Basel) 2021;11:1113. [PMID: 34207226 DOI: 10.3390/diagnostics11061113] [Reference Citation Analysis]
210 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]
211 Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ;  SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ. 2020;370:m3210. [PMID: 32907797 DOI: 10.1136/bmj.m3210] [Cited by in Crossref: 29] [Cited by in F6Publishing: 31] [Article Influence: 14.5] [Reference Citation Analysis]
212 Lee D, Yoon SN. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int J Environ Res Public Health 2021;18:E271. [PMID: 33401373 DOI: 10.3390/ijerph18010271] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
213 Leung FW, Hsieh YH. Artificial intelligence (computer-assisted detection) is the most recent novel approach to increase adenoma detection. Gastrointest Endosc 2021;93:86-8. [PMID: 33353642 DOI: 10.1016/j.gie.2020.07.059] [Reference Citation Analysis]
214 Gubatan J, Levitte S, Patel A, Balabanis T, Wei MT, Sinha SR. Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions. World J Gastroenterol 2021; 27(17): 1920-1935 [PMID: 34007130 DOI: 10.3748/wjg.v27.i17.1920] [Cited by in CrossRef: 10] [Cited by in F6Publishing: 5] [Article Influence: 10.0] [Reference Citation Analysis]
215 Barua I, Vinsard DG, Jodal HC, Løberg M, Kalager M, Holme Ø, Misawa M, Bretthauer M, Mori Y. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis. Endoscopy 2021;53:277-84. [DOI: 10.1055/a-1201-7165] [Cited by in Crossref: 17] [Cited by in F6Publishing: 15] [Article Influence: 8.5] [Reference Citation Analysis]
216 Goyal H, Sherazi SAA, Mann R, Gandhi Z, Perisetti A, Aziz M, Chandan S, Kopel J, Tharian B, Sharma N, Thosani N. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021;13:5494. [PMID: 34771658 DOI: 10.3390/cancers13215494] [Reference Citation Analysis]
217 Wu J, Chen J, Cai J. Application of Artificial Intelligence in Gastrointestinal Endoscopy. J Clin Gastroenterol. 2021;55:110-120. [PMID: 32925304 DOI: 10.1097/mcg.0000000000001423] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
218 Chang WY, Chiu HM. Can image-enhanced endoscopy improve adenoma detection rate? Dig Endosc 2021. [PMID: 34351014 DOI: 10.1111/den.14102] [Reference Citation Analysis]
219 Kleppe A, Skrede OJ, De Raedt S, Liestøl K, Kerr DJ, Danielsen HE. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 2021;21:199-211. [PMID: 33514930 DOI: 10.1038/s41568-020-00327-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
220 Wang KW, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects. World J Gastroenterol 2020; 26(34): 5090-5100 [PMID: 32982111 DOI: 10.3748/wjg.v26.i34.5090] [Cited by in CrossRef: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
221 May FP, Shaukat A. State of the Science on Quality Indicators for Colonoscopy and How to Achieve Them. Am J Gastroenterol 2020;115:1183-90. [DOI: 10.14309/ajg.0000000000000622] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
222 Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U. A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine 2020;126:104003. [DOI: 10.1016/j.compbiomed.2020.104003] [Cited by in Crossref: 13] [Cited by in F6Publishing: 7] [Article Influence: 6.5] [Reference Citation Analysis]
223 Murakami D, Yamato M, Amano Y, Tada T. Challenging detection of hard-to-find gastric cancers with artificial intelligence-assisted endoscopy. Gut. 2020;. [PMID: 32816967 DOI: 10.1136/gutjnl-2020-322453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
224 Matsui H, Kamba S, Horiuchi H, Takahashi S, Nishikawa M, Fukuda A, Tonouchi A, Kutsuna N, Shimahara Y, Tamai N, Sumiyama K. Detection Accuracy and Latency of Colorectal Lesions with Computer-Aided Detection System Based on Low-Bias Evaluation. Diagnostics (Basel) 2021;11:1922. [PMID: 34679619 DOI: 10.3390/diagnostics11101922] [Reference Citation Analysis]
225 Mohan BP, Facciorusso A, Khan SR, Chandan S, Kassab LL, Gkolfakis P, Tziatzios G, Triantafyllou K, Adler DG. Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials. EClinicalMedicine 2020;29-30:100622. [PMID: 33294821 DOI: 10.1016/j.eclinm.2020.100622] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
226 Calita M, Popa P, Cherciu Harbiyeli IF, Iordache S, Ciocalteu A, Filip MM, Saftoiu A. EndoCuff-Assisted Colonoscopy Versus Standard Colonoscopy in Colonic Polyp Detection-Experience from a Single Tertiary Centre. Curr Health Sci J 2021;47:33-41. [PMID: 34211745 DOI: 10.12865/CHSJ.47.01.06] [Reference Citation Analysis]
227 . Ueg Week 2020 Poster Presentations. United European Gastroenterol j 2020;8:144-887. [DOI: 10.1177/2050640620927345] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
228 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]
229 Carleton NM, Thakkar S. How to Approach and Interpret Studies on AI in Gastroenterology. Gastroenterology 2020;159:428-432.e1. [DOI: 10.1053/j.gastro.2020.04.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
230 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]
231 Ibrahim H, Liu X, Denniston AK. Reporting guidelines for artificial intelligence in healthcare research. Clin Exp Ophthalmol 2021;49:470-6. [PMID: 33956386 DOI: 10.1111/ceo.13943] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
232 Sánchez-Peralta LF, Picón A, Sánchez-Margallo FM, Pagador JB. Unravelling the effect of data augmentation transformations in polyp segmentation. Int J Comput Assist Radiol Surg 2020;15:1975-88. [PMID: 32989680 DOI: 10.1007/s11548-020-02262-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
233 Yang YJ. The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements. Clin Endosc. 2020;53:387-394. [PMID: 32668529 DOI: 10.5946/ce.2020.133] [Cited by in Crossref: 8] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
234 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]
235 Holzwanger EA, Bilal M, Glissen Brown JR, Singh S, Becq A, Ernest-Suarez K, Berzin TM. Benchmarking definitions of false-positive alerts during computer-aided polyp detection in colonoscopy. Endoscopy 2021;53:937-40. [PMID: 33137833 DOI: 10.1055/a-1302-2942] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
236 Antonelli G, Badalamenti M, Hassan C, Repici A. Impact of artificial intelligence on colorectal polyp detection. Best Pract Res Clin Gastroenterol 2021;52-53:101713. [PMID: 34172246 DOI: 10.1016/j.bpg.2020.101713] [Reference Citation Analysis]