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Cited by in F6Publishing
For: Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai A, Takasawa K, Bolatkan A, Shozu K, Dozen A, Machino H, Takahashi S, Asada K, Komatsu M, Sese J, Kaneko S. Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers (Basel). 2020;12. [PMID: 33256107 DOI: 10.3390/cancers12123532] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 9.5] [Reference Citation Analysis]
Number Citing Articles
1 Komatsu M, Sakai A, Dozen A, Shozu K, Yasutomi S, Machino H, Asada K, Kaneko S, Hamamoto R. Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging. Biomedicines 2021;9:720. [PMID: 34201827 DOI: 10.3390/biomedicines9070720] [Reference Citation Analysis]
2 Cheong KH, Tang KJW, Zhao X, Koh JEW, Faust O, Gururajan R, Ciaccio EJ, Rajinikanth V, Acharya UR. An automated skin melanoma detection system with melanoma-index based on entropy features. Biocybernetics and Biomedical Engineering 2021;41:997-1012. [DOI: 10.1016/j.bbe.2021.05.010] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
3 Giulietti M, Cecati M, Sabanovic B, Scirè A, Cimadamore A, Santoni M, Montironi R, Piva F. The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors. Diagnostics (Basel) 2021;11:206. [PMID: 33573278 DOI: 10.3390/diagnostics11020206] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
4 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]
5 Takahashi Y, Sone K, Noda K, Yoshida K, Toyohara Y, Kato K, Inoue F, Kukita A, Taguchi A, Nishida H, Miyamoto Y, Tanikawa M, Tsuruga T, Iriyama T, Nagasaka K, Matsumoto Y, Hirota Y, Hiraike-Wada O, Oda K, Maruyama M, Osuga Y, Fujii T. Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy. PLoS One 2021;16:e0248526. [PMID: 33788887 DOI: 10.1371/journal.pone.0248526] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
6 Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Current Oncology 2022;29:1773-95. [DOI: 10.3390/curroncol29030146] [Reference Citation Analysis]
7 Laptev VA, Ershova IV, Feyzrakhmanova DR. Medical Applications of Artificial Intelligence (Legal Aspects and Future Prospects). Laws 2022;11:3. [DOI: 10.3390/laws11010003] [Reference Citation Analysis]
8 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]
9 Takahashi S, Takahashi M, Tanaka S, Takayanagi S, Takami H, Yamazawa E, Nambu S, Miyake M, Satomi K, Ichimura K, Narita Y, Hamamoto R. A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning. Biomolecules 2021;11:565. [PMID: 33921457 DOI: 10.3390/biom11040565] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021;11:886. [PMID: 34575663 DOI: 10.3390/jpm11090886] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Takahashi S, Takahashi M, Kinoshita M, Miyake M, Kawaguchi R, Shinojima N, Mukasa A, Saito K, Nagane M, Otani R, Higuchi F, Tanaka S, Hata N, Tamura K, Tateishi K, Nishikawa R, Arita H, Nonaka M, Uda T, Fukai J, Okita Y, Tsuyuguchi N, Kanemura Y, Kobayashi K, Sese J, Ichimura K, Narita Y, Hamamoto R. Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities. Cancers (Basel) 2021;13:1415. [PMID: 33808802 DOI: 10.3390/cancers13061415] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
12 Hamamoto R. Application of Artificial Intelligence for Medical Research. Biomolecules 2021;11:90. [PMID: 33445802 DOI: 10.3390/biom11010090] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
13 Ono S, Komatsu M, Sakai A, Arima H, Ochida M, Aoyama R, Yasutomi S, Asada K, Kaneko S, Sasano T, Hamamoto R. Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning. Biomedicines 2022;10:1082. [DOI: 10.3390/biomedicines10051082] [Reference Citation Analysis]
14 Ghosh NK, Kumar A. Colorectal cancer: Artificial intelligence and its role in surgical decision making. Artif Intell Gastroenterol 2022; 3(2): 36-45 [DOI: 10.35712/aig.v3.i2.36] [Reference Citation Analysis]
15 Kaneko S, Takasawa K, Asada K, Shinkai N, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Komatsu M, Hamamoto R. Epigenetic Mechanisms Underlying COVID-19 Pathogenesis. Biomedicines 2021;9:1142. [PMID: 34572329 DOI: 10.3390/biomedicines9091142] [Reference Citation Analysis]
16 Sone K, Toyohara Y, Taguchi A, Miyamoto Y, Tanikawa M, Uchino-Mori M, Iriyama T, Tsuruga T, Osuga Y. Application of artificial intelligence in gynecologic malignancies: A review. J Obstet Gynaecol Res 2021;47:2577-85. [PMID: 33973305 DOI: 10.1111/jog.14818] [Reference Citation Analysis]
17 Alzubaidi L, Al-Amidie M, Al-Asadi A, Humaidi AJ, Al-Shamma O, Fadhel MA, Zhang J, Santamaría J, Duan Y. Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data. Cancers (Basel) 2021;13:1590. [PMID: 33808207 DOI: 10.3390/cancers13071590] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 8.0] [Reference Citation Analysis]
18 Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27(21): 2818-2833 [PMID: 34135556 DOI: 10.3748/wjg.v27.i21.2818] [Reference Citation Analysis]
19 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]
20 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]
21 Bosmans H, Zanca F, Gelaude F. Procurement, commissioning and QA of AI based solutions: An MPE's perspective on introducing AI in clinical practice. Phys Med 2021;83:257-63. [PMID: 33984579 DOI: 10.1016/j.ejmp.2021.04.006] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Asada K, Kaneko S, Takasawa K, Machino H, Takahashi S, Shinkai N, Shimoyama R, Komatsu M, Hamamoto R. Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology. Front Oncol 2021;11:666937. [PMID: 34055633 DOI: 10.3389/fonc.2021.666937] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
23 Asada K, Takasawa K, Machino H, Takahashi S, Shinkai N, Bolatkan A, Kobayashi K, Komatsu M, Kaneko S, Okamoto K, Hamamoto R. Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research. Biomedicines 2021;9:1513. [PMID: 34829742 DOI: 10.3390/biomedicines9111513] [Reference Citation Analysis]
24 Imagawa K, Shiomoto K. Performance change with the number of training data: A case study on the binary classification of COVID-19 chest X-ray by using convolutional neural networks. Comput Biol Med 2022;142:105251. [PMID: 35093727 DOI: 10.1016/j.compbiomed.2022.105251] [Reference Citation Analysis]
25 Shozu K, Komatsu M, Sakai A, Komatsu R, Dozen A, Machino H, Yasutomi S, Arakaki T, Asada K, Kaneko S, Matsuoka R, Nakashima A, Sekizawa A, Hamamoto R. Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos. Biomolecules 2020;10:E1691. [PMID: 33348873 DOI: 10.3390/biom10121691] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
26 Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives. Br J Cancer 2021. [PMID: 34837074 DOI: 10.1038/s41416-021-01633-1] [Reference Citation Analysis]
27 Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021;49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
28 Rafique R, Islam SMR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021;19:4003-17. [PMID: 34377366 DOI: 10.1016/j.csbj.2021.07.003] [Reference Citation Analysis]