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Cited by in F6Publishing
For: Nishida N, Yamakawa M, Shiina T, Kudo M. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatol Int 2019;13:416-21. [PMID: 30790230 DOI: 10.1007/s12072-019-09937-4] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
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1 Xia D, Chen G, Wu K, Yu M, Zhang Z, Lu Y, Xu L, Wang Y. Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011–2021: A bibliometric analysis. Front Public Health 2022;10:990708. [DOI: 10.3389/fpubh.2022.990708] [Reference Citation Analysis]
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5 Nishida N, Yamakawa M, Shiina T, Mekada Y, Nishida M, Sakamoto N, Nishimura T, Iijima H, Hirai T, Takahashi K, Sato M, Tateishi R, Ogawa M, Mori H, Kitano M, Toyoda H, Ogawa C, Kudo M; JSUM A. I. investigators. Artificial intelligence (AI) models for the ultrasonographic diagnosis of liver tumors and comparison of diagnostic accuracies between AI and human experts. J Gastroenterol. [DOI: 10.1007/s00535-022-01849-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
6 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] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Xiang K, Jiang B, Shang D. The overview of the deep learning integrated into the medical imaging of liver: a review. Hepatol Int 2021;15:868-80. [PMID: 34264509 DOI: 10.1007/s12072-021-10229-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
8 Tiyarattanachai T, Apiparakoon T, Marukatat S, Sukcharoen S, Geratikornsupuk N, Anukulkarnkusol N, Mekaroonkamol P, Tanpowpong N, Sarakul P, Rerknimitr R, Chaiteerakij R. Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. PLoS One 2021;16:e0252882. [PMID: 34101764 DOI: 10.1371/journal.pone.0252882] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
9 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: 5] [Article Influence: 0.5] [Reference Citation Analysis]
10 Yamakawa M, Shiina T, Nishida N, Kudo M. Optimal cropping for input images used in a convolutional neural network for ultrasonic diagnosis of liver tumors. Jpn J Appl Phys 2020;59:SKKE09. [DOI: 10.35848/1347-4065/ab80dd] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]