Open Access
Issue
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
Article Number 03005
Number of page(s) 5
Section Health Development
DOI https://doi.org/10.1051/e3sconf/202449103005
Published online 21 February 2024
  1. Raquel Dias and Ali Torkamani. Artificial intelligence in clinical and genomic diagnostics. Dias and Torkamani Genome Medicine. 2019;11(70):1-12. [CrossRef] [Google Scholar]
  2. MunetoshiAkazawa, Kazunori Hashimoto. Artificial Intelligence in Ovarian Cancer Diagnosis. Anticancer Res. 2020;40(8):4795-4800. [CrossRef] [PubMed] [Google Scholar]
  3. Lu M, Fan Z, Xu B, Chen L, Zheng X, Li J, Znati T, Mi Q, Jiang J. Using machine learning to predict ovarian cancer. Int J Med Inform. 2020;141:104195. doi: 10.1016/j.ijmedinf.2020.104195. [CrossRef] [PubMed] [Google Scholar]
  4. Kenbun Sone, Yusuke Toyohara, Ayumi Taguchi, Yuichiro Miyamoto, Michihiro Tanikawa, Mayuyo Uchino-Mori et al. Application of artificial intelligence in gynecologic malignancies: A review. Journal of Obstetrics and Gynaecology Research. 2021;47(8):2577–2585. [CrossRef] [PubMed] [Google Scholar]
  5. Javadi S, Mirroshandel SA. A novel deep learning method for automatic assessment of human sperm images. Comput Biol Med. 2019;109:182-194. [CrossRef] [PubMed] [Google Scholar]
  6. McCallum C, Riordon J, Wang Y, et al. Deep learning-based selection of human sperm with high DNA integrity. Commun Biol. 2019;2:250-259. [CrossRef] [PubMed] [Google Scholar]
  7. Dimitriadis I, L Bormann C, Kanakasabapathy MK, Thirumalaraju P, Kandula H, Yogesh V, Gudipati N, Natarajan V, C Petrozza J, Shafiee H. Automated smartphonebased system for measuring sperm viability, DNA fragmentation, and hyaluronic binding assay score. PLoS One. 2019;14(3):e0212562. [CrossRef] [PubMed] [Google Scholar]
  8. Zhan Q, Sierra ET, Malmsten J, Ye Z, Rosenwaks Z, Zaninovic N. Blastocyst score, a blastocyst quality ranking tool, is a predictor of blastocyst ploidy and implantation potential. F S Rep. 2020;1(2):133-141. [PubMed] [Google Scholar]
  9. Manna C, Nanni L, Lumini A, Pappalardo S. Artificial intelligence techniques for embryo and oocyte classification. Reprod Biomed Online. 2013 Jan;26(1):42-9. [CrossRef] [PubMed] [Google Scholar]
  10. Eraslan G, Avsec Z, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019 Jul;20(7):389-403. [CrossRef] [PubMed] [Google Scholar]
  11. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, Moreira AL, Razavian N, Tsirigos A. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018 Oct;24(10):1559-1567. [CrossRef] [PubMed] [Google Scholar]
  12. Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11(1):70. Published 2019 Nov 19. [CrossRef] [PubMed] [Google Scholar]
  13. Quang D, Xie X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 2016 Jun 20;44(11):e107. [CrossRef] [PubMed] [Google Scholar]
  14. Lukanova A, Kaaks R. Endogenous hormones and ovarian cancer: epidemiology and current hypotheses. Cancer EpidemiolPrev Biomarkers. 2005;14(1):98–107. [CrossRef] [PubMed] [Google Scholar]
  15. Lu Z, Chen J. [Introduction of WHO classification of tumours of female reproductive organs, fourth edition]. Zhonghua Bing Li XueZaZhi. 2014 Oct;43(10):649-50. [Google Scholar]
  16. Malvezzi M, Carioli G, Rodriguez T, Negri E, La Vecchia C. Global trends and predictions in ovarian cancer mortality. Ann Oncol. 2016;27(11):2017–2025. [CrossRef] [PubMed] [Google Scholar]
  17. Zheng G, Yu H, Kanerva A, Försti A, Sundquist K, Hemminki K. Familial risks of ovarian cancer by age at diagnosis, proband type and histology. PLoS One. 2018;13(10):e0205000. [Google Scholar]
  18. Terauchi, Fumitoshi ; Ishikawa, Takahisa ; Omura, Ryoko ; Moritake, Tetsuya ; Kato, Rina ; Sagawa, Yasukazu ; Nishi, Hirotaka ; Ito, Hiroe ; Isaka, Keiichi. Effect of the N Factor on the Prognosis of pT3C Ovarian Cancer With Optimal Debulking Surgery. Clinical Ovarian and Other Gynecologic Cancer. 2013;6(1-2):36–41. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.