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For: 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]
Number Citing Articles
1 Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 2021;21:10. [PMID: 33407169 DOI: 10.1186/s12876-020-01585-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
2 Kou W, Carlson DA, Baumann AJ, Donnan EN, Schauer JM, Etemadi M, Pandolfino JE. A multi-stage machine learning model for diagnosis of esophageal manometry. Artificial Intelligence in Medicine 2022;124:102233. [DOI: 10.1016/j.artmed.2021.102233] [Reference Citation Analysis]
3 Choi SJ, Kim ES, Choi K. Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms. Sci Rep 2021;11:5311. [PMID: 33674628 DOI: 10.1038/s41598-021-84299-2] [Reference Citation Analysis]
4 Konikoff T, Goren I, Yalon M, Tamir S, Avni-Biron I, Yanai H, Dotan I, Ollech JE. Machine learning for selecting patients with Crohn's disease for abdominopelvic computed tomography in the emergency department. Dig Liver Dis 2021:S1590-8658(21)00334-0. [PMID: 34253482 DOI: 10.1016/j.dld.2021.06.020] [Reference Citation Analysis]
5 Litvin A, Korenev S, Rumovskaya S, Sartelli M, Baiocchi G, Biffl WL, Coccolini F, Di Saverio S, Kelly MD, Kluger Y, Leppäniemi A, Sugrue M, Catena F. WSES project on decision support systems based on artificial neural networks in emergency surgery. World J Emerg Surg 2021;16:50. [PMID: 34565420 DOI: 10.1186/s13017-021-00394-9] [Reference Citation Analysis]
6 Limdi JK, Farraye FA. Automated endoscopic assessment in ulcerative colitis: the next frontier. Gastrointest Endosc 2021;93:737-9. [PMID: 33583524 DOI: 10.1016/j.gie.2020.10.032] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Ponnoprat D, Inkeaw P, Chaijaruwanich J, Traisathit P, Sripan P, Inmutto N, Na Chiangmai W, Pongnikorn D, Chitapanarux I. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans.Med Biol Eng Comput. 2020;58:2497-2515. [PMID: 32794015 DOI: 10.1007/s11517-020-02229-2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
8 Con D, van Langenberg DR, Vasudevan A. Deep learning vs conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study. World J Gastroenterol 2021; 27(38): 6476-6488 [PMID: 34720536 DOI: 10.3748/wjg.v27.i38.6476] [Reference Citation Analysis]
9 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]
10 Ardevol A, Alvarado-Tapias E, Garcia-Guix M, Brujats A, Gonzalez L, Hernández-Gea V, Aracil C, Pavel O, Cuyas B, Graupera I, Colomo A, Poca M, Torras X, Concepción M, Villanueva C. Early rebleeding increases mortality of variecal bleeders on secondary prophylaxis with β-blockers and ligation. Dig Liver Dis 2020;52:1017-25. [PMID: 32653417 DOI: 10.1016/j.dld.2020.06.005] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
11 Storås AM, Strümke I, Riegler MA, Grauslund J, Hammer HL, Yazidi A, Halvorsen P, Gundersen KG, Utheim TP, Jackson CJ. Artificial intelligence in dry eye disease. Ocul Surf 2021;23:74-86. [PMID: 34843999 DOI: 10.1016/j.jtos.2021.11.004] [Reference Citation Analysis]
12 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]
13 Fonollà R, van der Zander QEW, Schreuder RM, Subramaniam S, Bhandari P, Masclee AAM, Schoon EJ, van der Sommen F, de With PHN. Automatic image and text-based description for colorectal polyps using BASIC classification. Artif Intell Med 2021;121:102178. [PMID: 34763800 DOI: 10.1016/j.artmed.2021.102178] [Reference Citation Analysis]
14 Skamnelos A, Lazaridis N, Vlachou E, Koukias N, Apostolopoulos P, Murino A, Christodoulou D, Despott EJ. The role of small-bowel endoscopy in inflammatory bowel disease: an updated review on the state-of-the-art in 2021. Ann Gastroenterol 2021;34:599-611. [PMID: 34475730 DOI: 10.20524/aog.2021.0652] [Reference Citation Analysis]
15 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]
16 Ogata N, Ohtsuka K, Ogawa M, Maeda Y, Ishida F, Kudo SE. Image-Enhanced Capsule Endoscopy Improves the Identification of Small Intestinal Lesions. Diagnostics (Basel) 2021;11:2122. [PMID: 34829469 DOI: 10.3390/diagnostics11112122] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Arista Romeu EJ, Rivera Fernández JD, Roa Tort K, Valor A, Escobedo G, Fabila Bustos DA, Stolik S, de la Rosa JM, Guzmán C. Combined methods of optical spectroscopy and artificial intelligence in the assessment of experimentally induced non-alcoholic fatty liver. Comput Methods Programs Biomed 2021;198:105777. [PMID: 33069975 DOI: 10.1016/j.cmpb.2020.105777] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Tsai CL, Mukundan A, Chung CS, Chen YH, Wang YK, Chen TH, Tseng YS, Huang CW, Wu IC, Wang HC. Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer. Cancers (Basel) 2021;13:4593. [PMID: 34572819 DOI: 10.3390/cancers13184593] [Reference Citation Analysis]
19 Solitano V, D'Amico F, Allocca M, Fiorino G, Zilli A, Loy L, Gilardi D, Radice S, Correale C, Danese S, Peyrin-Biroulet L, Furfaro F. Rediscovering histology: what is new in endoscopy for inflammatory bowel disease? Therap Adv Gastroenterol 2021;14:17562848211005692. [PMID: 33948114 DOI: 10.1177/17562848211005692] [Reference Citation Analysis]
20 Iacucci M, Jeffery L, Acharjee A, Nardone OM, Zardo D, Smith SCL, Bazarova A, Cannatelli R, Shivaji UN, Williams J, Gkoutos G, Ghosh S. Ultra-high Magnification Endocytoscopy and Molecular Markers for Defining Endoscopic and Histologic Remission in Ulcerative Colitis-An Exploratory Study to Define Deep Remission. Inflamm Bowel Dis 2021:izab059. [PMID: 34019073 DOI: 10.1093/ibd/izab059] [Reference Citation Analysis]
21 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]
22 Aguilar C, Regensburger AP, Knieling F, Wagner AL, Siebenlist G, Woelfle J, Koehler H, Hoerning A, Jüngert J. Pediatric Buried Bumper Syndrome: Diagnostic Validity of Transabdominal Ultrasound and Artificial Intelligence. Ultraschall Med 2021. [PMID: 34034349 DOI: 10.1055/a-1471-3039] [Reference Citation Analysis]
23 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]
24 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-80. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
25 Tian L, Hunt B, Bell MAL, Yi J, Smith JT, Ochoa M, Intes X, Durr NJ. Deep Learning in Biomedical Optics. Lasers Surg Med 2021;53:748-75. [PMID: 34015146 DOI: 10.1002/lsm.23414] [Reference Citation Analysis]
26 Li J, Qian JM. Artificial intelligence in inflammatory bowel disease: current status and opportunities. Chin Med J (Engl). 2020;133:757-759. [PMID: 32132365 DOI: 10.1097/cm9.0000000000000714] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
27 Kou W, Galal GO, Klug MW, Mukhin V, Carlson DA, Etemadi M, Kahrilas PJ, Pandolfino JE. Deep learning-based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry. Neurogastroenterol Motil 2021;:e14290. [PMID: 34709712 DOI: 10.1111/nmo.14290] [Reference Citation Analysis]
28 Goyal H, Mann R, Gandhi Z, Perisetti A, Zhang Z, Sharma N, Saligram S, Inamdar S, Tharian B. Application of artificial intelligence in pancreaticobiliary diseases.Ther Adv Gastrointest Endosc. 2021;14:2631774521993059. [PMID: 33644756 DOI: 10.1177/2631774521993059] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
29 Lorenzo-Zúñiga V, Bustamante-Balén M, Pons-Beltrán V, Peña-Gil C. Development of knowledge-based clinical decision support system for patients included in colorectal screening program. Gastroenterol Hepatol 2021:S0210-5705(21)00194-1. [PMID: 34118316 DOI: 10.1016/j.gastrohep.2021.05.011] [Reference Citation Analysis]
30 Majam M, Phatsoane M, Hanna K, Faul C, Arora L, Makthal S, Kumar A, Jois K, Lalla-Edward ST. Utility of a Machine-Guided Tool for Assessing Risk Behavior Associated With Contracting HIV in Three Sites in South Africa: Protocol for an In-Field Evaluation. JMIR Res Protoc 2021;10:e30304. [PMID: 34860679 DOI: 10.2196/30304] [Reference Citation Analysis]
31 Bhandari P, Longcroft-Wheaton G, Libanio D, Pimentel-Nunes P, Albeniz E, Pioche M, Sidhu R, Spada C, Anderloni A, Repici A, Haidry R, Barthet M, Neumann H, Antonelli G, Testoni A, Ponchon T, Siersema PD, Fuccio L, Hassan C, Dinis-Ribeiro M. Revising the European Society of Gastrointestinal Endoscopy (ESGE) research priorities: a research progress update. Endoscopy 2021;53:535-54. [PMID: 33822332 DOI: 10.1055/a-1397-3005] [Reference Citation Analysis]
32 Zhong BY, Yan ZP, Sun JH, Zhang L, Hou ZH, Yang MJ, Zhou GH, Wang WS, Li Z, Huang P, Zhang S, Zhu XL, Ni CF. Prognostic Performance of Albumin-Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib. Front Oncol 2020;10:525461. [PMID: 33392064 DOI: 10.3389/fonc.2020.525461] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
33 Diehl DL. Artificial intelligence applications in EUS: the journey of a thousand miles begins with a single step. Gastrointest Endosc 2021;93:1131-2. [PMID: 33685626 DOI: 10.1016/j.gie.2020.09.034] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Sullivan P, Gupta S, Powers PD, Marya NB. Artificial Intelligence Research and Development for Application in Video Capsule Endoscopy. Gastrointest Endosc Clin N Am 2021;31:387-97. [PMID: 33743933 DOI: 10.1016/j.giec.2020.12.009] [Reference Citation Analysis]
35 Ravi K. Artificial intelligence: finding the intersection of predictive modeling and clinical utility. Gastrointest Endosc 2021;93:1273-5. [PMID: 33691975 DOI: 10.1016/j.gie.2020.12.008] [Reference Citation Analysis]
36 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]
37 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Reference Citation Analysis]
38 Marlicz W, Koulaouzidis G, Koulaouzidis A. Artificial Intelligence in Gastroenterology-Walking into the Room of Little Miracles. J Clin Med 2020;9:E3675. [PMID: 33207649 DOI: 10.3390/jcm9113675] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
39 Ghoshal UC, Rai S, Kulkarni A, Gupta A. Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence. JGH Open. 2020;4:889-897. [PMID: 33102760 DOI: 10.1002/jgh3.12342] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
40 van der Laan JJH, van der Waaij AM, Gabriëls RY, Festen EAM, Dijkstra G, Nagengast WB. Endoscopic imaging in inflammatory bowel disease: current developments and emerging strategies. Expert Rev Gastroenterol Hepatol 2021;15:115-26. [PMID: 33094654 DOI: 10.1080/17474124.2021.1840352] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
41 Lee HW, Sung JJY, Ahn SH. Artificial intelligence in liver disease. J Gastroenterol Hepatol 2021;36:539-42. [PMID: 33709605 DOI: 10.1111/jgh.15409] [Reference Citation Analysis]
42 Zhou Q, Chen ZH, Cao YH, Peng S. Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review. NPJ Digit Med 2021;4:154. [PMID: 34711955 DOI: 10.1038/s41746-021-00524-2] [Reference Citation Analysis]
43 Lu YF, Lyu B. Current situation and prospect of artificial intelligence application in endoscopic diagnosis of Helicobacter pylori infection. Artif Intell Gastrointest Endosc 2021; 2(3): 50-62 [DOI: 10.37126/aige.v2.i3.50] [Reference Citation Analysis]
44 Tang S, Lin L, Cheng J, Zhao J, Xuan Q, Shao J, Zhou Y, Zhang Y. The prognostic value of preoperative fibrinogen-to-prealbumin ratio and a novel FFC score in patients with resectable gastric cancer. BMC Cancer 2020;20:382. [PMID: 32375697 DOI: 10.1186/s12885-020-06866-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
45 Robertson AR, Koulaouzidis A, Rondonotti E, Bruno M, Pennazio M. The Role of Video Capsule Endoscopy in Liver Disease. Gastrointest Endosc Clin N Am 2021;31:363-76. [PMID: 33743931 DOI: 10.1016/j.giec.2020.12.007] [Reference Citation Analysis]
46 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]
47 Pecere S, Milluzzo SM, Esposito G, Dilaghi E, Telese A, Eusebi LH. Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases. Diagnostics (Basel) 2021;11:1575. [PMID: 34573917 DOI: 10.3390/diagnostics11091575] [Reference Citation Analysis]
48 Masuzaki R, Kanda T, Sasaki R, Matsumoto N, Nirei K, Ogawa M, Moriyama M. Application of artificial intelligence in hepatology: Minireview. Artif Intell Gastroenterol 2020; 1(1): 5-11 [DOI: 10.35712/aig.v1.i1.5] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
49 Bojarski C, Waldner M, Rath T, Schürmann S, Neurath MF, Atreya R, Siegmund B. Innovative Diagnostic Endoscopy in Inflammatory Bowel Diseases: From High-Definition to Molecular Endoscopy. Front Med (Lausanne) 2021;8:655404. [PMID: 34368180 DOI: 10.3389/fmed.2021.655404] [Reference Citation Analysis]
50 Solitano V, Alfarone L, D'Amico F, Peyrin-Biroulet L, Danese S. IBD goes home: from telemedicine to self-administered advanced therapies. Expert Opin Biol Ther 2021;:1-13. [PMID: 34116611 DOI: 10.1080/14712598.2021.1942833] [Reference Citation Analysis]
51 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]
52 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]
53 Cummins G. Smart pills for gastrointestinal diagnostics and therapy. Adv Drug Deliv Rev 2021;177:113931. [PMID: 34416311 DOI: 10.1016/j.addr.2021.113931] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
54 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]
55 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]
56 Olveres J, González G, Torres F, Moreno-Tagle JC, Carbajal-Degante E, Valencia-Rodríguez A, Méndez-Sánchez N, Escalante-Ramírez B. What is new in computer vision and artificial intelligence in medical image analysis applications. Quant Imaging Med Surg 2021;11:3830-53. [PMID: 34341753 DOI: 10.21037/qims-20-1151] [Reference Citation Analysis]
57 Kernebeck S, Busse TS, Böttcher MD, Weitz J, Ehlers J, Bork U. Impact of mobile health and medical applications on clinical practice in gastroenterology. World J Gastroenterol 2020; 26(29): 4182-4197 [PMID: 32848328 DOI: 10.3748/wjg.v26.i29.4182] [Cited by in CrossRef: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
58 Saraiva MM, Ribeiro T, Ferreira JPS, Boas FV, Afonso J, Santos AL, Parente MPL, Jorge RN, Pereira P, Macedo G. Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study. Gastrointest Endosc 2021:S0016-5107(21)01619-9. [PMID: 34508767 DOI: 10.1016/j.gie.2021.08.027] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
59 Ferrarese A, Sartori G, Orrù G, Frigo AC, Pelizzaro F, Burra P, Senzolo M. Machine learning in liver transplantation: a tool for some unsolved questions? Transpl Int 2021;34:398-411. [PMID: 33428298 DOI: 10.1111/tri.13818] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
60 Guleria S, Shah TU, Pulido JV, Fasullo M, Ehsan L, Lippman R, Sali R, Mutha P, Cheng L, Brown DE, Syed S. Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy. Sci Rep 2021;11:5086. [PMID: 33658592 DOI: 10.1038/s41598-021-84510-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
61 Neuberger J, Cain O. The Need for Alternatives to Liver Biopsies: Non-Invasive Analytics and Diagnostics. Hepat Med 2021;13:59-69. [PMID: 34163263 DOI: 10.2147/HMER.S278076] [Reference Citation Analysis]
62 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]
63 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]
64 Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction . Artif Intell Gastroenterol 2021; 2(2): 56-68 [DOI: 10.35712/aig.v2.i2.56] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
65 Piccialli F, Somma VD, Giampaolo F, Cuomo S, Fortino G. A survey on deep learning in medicine: Why, how and when? Information Fusion 2021;66:111-37. [DOI: 10.1016/j.inffus.2020.09.006] [Cited by in Crossref: 26] [Cited by in F6Publishing: 4] [Article Influence: 26.0] [Reference Citation Analysis]
66 Vaz K, Goodwin T, Kemp W, Roberts S, Majeed A. Artificial Intelligence in Hepatology: A Narrative Review. Semin Liver Dis 2021. [PMID: 34327698 DOI: 10.1055/s-0041-1731706] [Reference Citation Analysis]
67 Kim HS, Peng FB, Gomez Cifuentes JD. Regarding: Shung et al: Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding. Gastroenterology 2020;158:2308-9. [PMID: 32201181 DOI: 10.1053/j.gastro.2020.01.055] [Reference Citation Analysis]
68 Kothari DJ, Sheth SG. Opportunity Is Knocking: Brainstorming Neural Networks for Management of Acute Pancreatitis. Pancreas 2021;50:e11-3. [PMID: 33370040 DOI: 10.1097/MPA.0000000000001716] [Reference Citation Analysis]
69 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]
70 Jiang Y, Liang X, Wang W, Chen C, Yuan Q, Zhang X, Li N, Chen H, Yu J, Xie Y, Xu Y, Zhou Z, Li G, Li R. Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning. JAMA Netw Open 2021;4:e2032269. [PMID: 33399858 DOI: 10.1001/jamanetworkopen.2020.32269] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
71 Liu J, Zhang J, Huang H, Wang Y, Zhang Z, Ma Y, He X. A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population. Front Pediatr 2021;9:756095. [PMID: 34820343 DOI: 10.3389/fped.2021.756095] [Reference Citation Analysis]
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