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For: Spann A, Yasodhara A, Kang J, Watt K, Wang B, Goldenberg A, Bhat M. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology. 2020;71:1093-1105. [PMID: 31907954 DOI: 10.1002/hep.31103] [Cited by in Crossref: 25] [Cited by in F6Publishing: 27] [Article Influence: 12.5] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Khan MQ, Watt KD. Scientific Relief: When Science and Technology Agree and Lead. Liver Transpl 2021;27:484-5. [PMID: 33522080 DOI: 10.1002/lt.25998] [Reference Citation Analysis]
3 Mahmud N, Goldberg DS, Bittermann T. Best Practices in Large Database Clinical Epidemiology Research in Hepatology: Barriers and Opportunities. Liver Transpl 2021. [PMID: 34265178 DOI: 10.1002/lt.26231] [Reference Citation Analysis]
4 Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol 2021;36:543-50. [PMID: 33709607 DOI: 10.1111/jgh.15385] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
5 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
6 Briceño J, Ayllón MD, Ciria R. Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching. Curr Opin Organ Transplant 2020;25:406-11. [PMID: 32487891 DOI: 10.1097/MOT.0000000000000781] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
7 Nitski O, Azhie A, Qazi-Arisar FA, Wang X, Ma S, Lilly L, Watt KD, Levitsky J, Asrani SK, Lee DS, Rubin BB, Bhat M, Wang B. Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data. Lancet Digit Health 2021;3:e295-305. [PMID: 33858815 DOI: 10.1016/S2589-7500(21)00040-6] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 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]
9 Balch JA, Delitto D, Tighe PJ, Zarrinpar A, Efron PA, Rashidi P, Upchurch GR Jr, Bihorac A, Loftus TJ. Machine Learning Applications in Solid Organ Transplantation and Related Complications. Front Immunol 2021;12:739728. [PMID: 34603324 DOI: 10.3389/fimmu.2021.739728] [Reference Citation Analysis]
10 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]
11 Dani A, Heidel JS, Qiu T, Zhang Y, Ni Y, Hossain MM, Chin C, Morales DLS, Huang B, Zafar F. External validation and comparison of risk score models in pediatric heart transplants. Pediatr Transplant 2021;:e14204. [PMID: 34881481 DOI: 10.1111/petr.14204] [Reference Citation Analysis]
12 Brüggenwirth IMA, Porte RJ, Martins PN. Bile Composition as a Diagnostic and Prognostic Tool in Liver Transplantation. Liver Transpl 2020;26:1177-87. [DOI: 10.1002/lt.25771] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
13 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]
14 García-Carretero R, Holgado-Cuadrado R, Barquero-Pérez Ó. Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest. Entropy (Basel) 2021;23:763. [PMID: 34204225 DOI: 10.3390/e23060763] [Reference Citation Analysis]
15 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]
16 Fu Q, Agarwal D, Deng K, Matheson R, Yang H, Wei L, Ran Q, Deng S, Markmann JF. An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation. Front Immunol 2021;12:695806. [PMID: 34305931 DOI: 10.3389/fimmu.2021.695806] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Atsawarungruangkit A, Elfanagely Y, Asombang AW, Rupawala A, Rich HG. Understanding deep learning in capsule endoscopy: Can artificial intelligence enhance clinical practice? Artif Intell Gastrointest Endosc 2020; 1(2): 33-43 [DOI: 10.37126/aige.v1.i2.33] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Briceno J. Artificial intelligence and organ transplantation: challenges and expectations. Current Opinion in Organ Transplantation 2020;Publish Ahead of Print. [DOI: 10.1097/mot.0000000000000775] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
19 Nishida N, Kudo M. Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor. Front Oncol 2020;10:594580. [PMID: 33409151 DOI: 10.3389/fonc.2020.594580] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
20 Yasodhara A, Dong V, Azhie A, Goldenberg A, Bhat M. Identifying Modifiable Predictors of Long-Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning. Liver Transpl 2021;27:536-47. [PMID: 33113221 DOI: 10.1002/lt.25930] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
21 Li YJ, Zhong KH, Bai XH, Tang X, Li P, Yang ZY, Zhi HY, Li XJ, Chen Y, Deng P, Qin XL, Gu JT, Ning JL, Lu KZ, Zhang J, Xia ZY, Chen YW, Yi B. A Simple and Quick Screening Method for Intrapulmonary Vascular Dilation in Cirrhotic Patients Based on Machine Learning. J Clin Transl Hepatol 2021;9:682-9. [PMID: 34722183 DOI: 10.14218/JCTH.2020.00184] [Reference Citation Analysis]
22 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]
23 Atsawarungruangkit A, Laoveeravat P, Promrat K. Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database. World J Hepatol 2021; 13(10): 1417-1427 [PMID: 34786176 DOI: 10.4254/wjh.v13.i10.1417] [Reference Citation Analysis]
24 Kwong A, Hameed B, Syed S, Ho R, Mard H, Arshad S, Ho I, Suleman T, Yao F, Mehta N. Machine learning to predict waitlist dropout among liver transplant candidates with hepatocellular carcinoma. Cancer Medicine. [DOI: 10.1002/cam4.4538] [Reference Citation Analysis]
25 Goudsmit BFJ, Putter H, Tushuizen ME, Vogelaar S, Pirenne J, Alwayn IPJ, van Hoek B, Braat AE. Refitting the Model for End-Stage Liver Disease for the Eurotransplant Region. Hepatology 2021;74:351-63. [PMID: 33301607 DOI: 10.1002/hep.31677] [Reference Citation Analysis]
26 Clement J, Maldonado AQ. Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant. Front Immunol 2021;12:694222. [PMID: 34177958 DOI: 10.3389/fimmu.2021.694222] [Reference Citation Analysis]
27 Chen C, Yang D, Gao S, Zhang Y, Chen L, Wang B, Mo Z, Yang Y, Hei Z, Zhou S. Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation. Respir Res 2021;22:94. [PMID: 33789673 DOI: 10.1186/s12931-021-01690-3] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Guijo-Rubio D, Gutiérrez PA, Hervás-Martínez C. Machine learning methods in organ transplantation. Curr Opin Organ Transplant 2020;25:399-405. [PMID: 32618714 DOI: 10.1097/MOT.0000000000000774] [Reference Citation Analysis]
29 Kim HY, Lampertico P, Nam JY, Lee HC, Kim SU, Sinn DH, Seo YS, Lee HA, Park SY, Lim YS, Jang ES, Yoon EL, Kim HS, Kim SE, Ahn SB, Shim JJ, Jeong SW, Jung YJ, Sohn JH, Cho YK, Jun DW, Dalekos GN, Idilman R, Sypsa V, Berg T, Buti M, Calleja JL, Goulis J, Manolakopoulos S, Janssen H, Jang MJ, Lee YB, Kim YJ, Yoon JH, Papatheodoridis GV, Lee JH. An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B. J Hepatol 2021:S0168-8278(21)02087-0. [PMID: 34606915 DOI: 10.1016/j.jhep.2021.09.025] [Reference Citation Analysis]
30 Okanoue T, Shima T, Mitsumoto Y, Umemura A, Yamaguchi K, Itoh Y, Yoneda M, Nakajima A, Mizukoshi E, Kaneko S, Harada K. Artificial intelligence/neural network system for the screening of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatol Res 2021;51:554-69. [PMID: 33594747 DOI: 10.1111/hepr.13628] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
31 Khorsandi SE, Hardgrave HJ, Osborn T, Klutts G, Nigh J, Spencer-Cole RT, Kakos CD, Anastasiou I, Mavros MN, Giorgakis E. Artificial Intelligence in Liver Transplantation. Transplant Proc 2021:S0041-1345(21)00741-7. [PMID: 34740449 DOI: 10.1016/j.transproceed.2021.09.045] [Reference Citation Analysis]