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Cited by in F6Publishing
For: Sato M, Morimoto K, Kajihara S, Tateishi R, Shiina S, Koike K, Yatomi Y. Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma. Sci Rep. 2019;9:7704. [PMID: 31147560 DOI: 10.1038/s41598-019-44022-8] [Cited by in Crossref: 26] [Cited by in F6Publishing: 25] [Article Influence: 8.7] [Reference Citation Analysis]
Number Citing Articles
1 Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2(2): 42-55 [DOI: 10.35712/aig.v2.i2.42] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 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]
3 Zhang L, Wang Y, Niu M, Wang C, Wang Z. Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study. Sci Rep 2020;10:4406. [PMID: 32157171 DOI: 10.1038/s41598-020-61123-x] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
4 Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, González-Díaz H. Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models. Nanoscale 2019;11:21811-23. [PMID: 31691701 DOI: 10.1039/c9nr05070a] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 4.5] [Reference Citation Analysis]
5 Wu Q, Nasoz F, Jung J, Bhattarai B, Han MV. Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men. Calcif Tissue Int 2020;107:353-61. [PMID: 32728911 DOI: 10.1007/s00223-020-00734-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Shen J, Qi L, Zou Z, Du J, Kong W, Zhao L, Wei J, Lin L, Ren M, Liu B. Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases. Sci Rep 2020;10:4435. [PMID: 32157118 DOI: 10.1038/s41598-020-61298-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
7 Ballotin VR, Bigarella LG, Soldera J, Soldera J. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artif Intell Gastrointest Endosc 2021; 2(4): 127-135 [DOI: 10.37126/aige.v2.i4.127] [Reference Citation Analysis]
8 Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F; Special Interest Group (SIG) Artificial Intelligence and Liver Diseases; Italian Association for the Study of the Liver (AISF). The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2021:S1590-8658(21)00317-0. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Reference Citation Analysis]
9 Geng L, Zong R, Shi Y, Xu K. Prognostic role of preoperative albumin-bilirubin grade on patients with hepatocellular carcinoma after surgical resection: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol. 2020;32:769-778. [PMID: 31834053 DOI: 10.1097/meg.0000000000001618] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
10 Alksas A, Shehata M, Saleh GA, Shaffie A, Soliman A, Ghazal M, Khelifi A, Khalifeh HA, Razek AA, Giridharan GA, El-Baz A. A novel computer-aided diagnostic system for accurate detection and grading of liver tumors. Sci Rep 2021;11:13148. [PMID: 34162893 DOI: 10.1038/s41598-021-91634-0] [Reference Citation Analysis]
11 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]
12 Ishii E, Ebner DK, Kimura S, Agha-Mir-Salim L, Uchimido R, Celi LA. The advent of medical artificial intelligence: lessons from the Japanese approach. J Intensive Care 2020;8:35. [PMID: 32467762 DOI: 10.1186/s40560-020-00452-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
13 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]
14 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]
15 Sato M, Tateishi R, Yatomi Y, Koike K. Artificial intelligence in the diagnosis and management of hepatocellular carcinoma.J Gastroenterol Hepatol. 2021;36:551-560. [PMID: 33709610 DOI: 10.1111/jgh.15413] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
16 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]
17 Zhu Y, Gao W, Guo Z, Zhou Y, Zhou Y. Liver tissue classification of en face images by fractal dimension-based support vector machine. J Biophotonics 2020;13:e201960154. [PMID: 31909553 DOI: 10.1002/jbio.201960154] [Reference Citation Analysis]
18 Niu M, Wang Y, Zhang L, Tu R, Liu X, Hou J, Huo W, Mao Z, Wang C, Bie R. Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China. Hypertens Res 2021;44:1483-91. [PMID: 34480134 DOI: 10.1038/s41440-021-00738-7] [Reference Citation Analysis]
19 Wu Q, Nasoz F, Jung J, Bhattarai B, Han MV, Greenes RA, Saag KG. Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men. Sci Rep 2021;11:4482. [PMID: 33627720 DOI: 10.1038/s41598-021-83828-3] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
20 Liu YX, Liu X, Cen C, Li X, Liu JM, Ming ZY, Yu SF, Tang XF, Zhou L, Yu J, Huang KJ, Zheng SS. Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: An extended study. Hepatobiliary Pancreat Dis Int 2021;20:409-15. [PMID: 34420885 DOI: 10.1016/j.hbpd.2021.08.004] [Reference Citation Analysis]
21 Lupsor-Platon M, Serban T, Silion AI, Tirpe GR, Tirpe A, Florea M. Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease. Cancers (Basel) 2021;13:790. [PMID: 33672827 DOI: 10.3390/cancers13040790] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
22 Shi W, Kuang S, Cao S, Hu B, Xie S, Chen S, Chen Y, Gao D, Zhu Y, Zhang H, Liu H, Ye M, Sirlin CB, Wang J. Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol. Abdom Radiol (NY). 2020;45:2688-2697. [PMID: 32232524 DOI: 10.1007/s00261-020-02485-8] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 10.0] [Reference Citation Analysis]
23 Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13(12): 1977-1990 [DOI: 10.4254/wjh.v13.i12.1977] [Reference Citation Analysis]
24 Kamogashira T, Fujimoto C, Kinoshita M, Kikkawa Y, Yamasoba T, Iwasaki S. Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability. Front Neurol 2020;11:7. [PMID: 32116997 DOI: 10.3389/fneur.2020.00007] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
25 Lee Y, Park Y, Kim C, Lee E, Lee HY, Woo SD, You SC, Park RW, Park HS. Longitudinal Outcomes of Severe Asthma: Real-World Evidence of Multidimensional Analyses. J Allergy Clin Immunol Pract 2021;9:1285-1294.e6. [PMID: 33049391 DOI: 10.1016/j.jaip.2020.09.055] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
26 Blidisel A, Marcovici I, Coricovac D, Hut F, Dehelean CA, Cretu OM. Experimental Models of Hepatocellular Carcinoma-A Preclinical Perspective. Cancers (Basel) 2021;13:3651. [PMID: 34359553 DOI: 10.3390/cancers13153651] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
27 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]
28 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]