The Advance Technology in Reproductive Medicine
Keywords:
Infertile, Artificial IntelligentAbstract
In this generation, where such a massive and rapid technology development has an immense impact on a number of fields, especially medical one, there is a cooperation between an advanced technology and a medical diagnosis to solve an infertile problem in order to increase a chance for people with such a problem.
This paper is to inform how to treat an infertile problem from an applied knowledge on Artificial Intelligence, as known as AI, in order for readers to be aware of advantages as well as limitations of such the technology with an attempt to further an appropriate use in the future.
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Singh A, Au J, Saeedi P & Havelock J. Automatic segmentation of trophectoderm in microscopic images of human blastocysts. IEEE Transactions on Bio- Medical Engineering. 2015. 62 382–393.
Stoop D, Ermini B, Polyzos NP, Haentjens P, De Vos M, Verheyen G & Devroey P. Reproductive potential of a metaphase II oocyte retrieved after ovarian stimulation: an analysis of 23 354 ICSI cycles. Human Reproduction. 2012. 27 2030–2035.
Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019.
Yanez LZ, Han J, Behr BB, Reijo Pera RA & Camarillo DB. Human oocyte developmental potential is predicted by mechanical properties within hours after fertilization. Nature Communications. 2016. 7 10809.
Cavalera F, Zanoni M, Merico V, Bui TTH, Belli M, Fassina L, Garagna S & Zuccotti M. A neural network-based identification of developmentally competent or incompetent mouse fully-grown oocytes. Journal of Visualized Experiments.
2018.
De Geyter C, Calhaz-Jorge C, Kupka MS, Wyns C, Mocanu E, Motrenko T, Scaravelli G, Smeenk J, Vidakovic S, Goossens V, et al. ART in Europe, 2014 : results generated from European registries by ESHRE : the European IVF-monitoring Consortium (EIM) for the European Society of Human Reproduction and Embryology (ESHRE). Human Reproduction. 2018. 33 1586–1601.
Goodson SG, Zhang Z, Tsuruta JK, Wang W & O’Brien DA. Classification of mouse sperm motility patterns using an automated multiclass support vector machines model. Biology of Reproduction. 2011. 84 1207–1215.
Kohli M, Prevedello LM, Filice RW & Geis JR. Implementing machine learning in radiology practice and research. American Journal of Roentgenology. 2017. 208 754–760.
Saeedi P, Yee D, Au J & Havelock J. Automatic identification of human blastocyst components via texture. IEEE Transactions on Bio-Medical Engineering. 2017. 64 2968–2978.
Singh A, Au J, Saeedi P & Havelock J. Automatic segmentation of trophectoderm in microscopic images of human blastocysts. IEEE Transactions on Bio- Medical Engineering. 2015. 62 382–393.
Stoop D, Ermini B, Polyzos NP, Haentjens P, De Vos M, Verheyen G & Devroey P. Reproductive potential of a metaphase II oocyte retrieved after ovarian stimulation: an analysis of 23 354 ICSI cycles. Human Reproduction. 2012. 27 2030–2035.
Wang R, Pan W, Jin L, Li Y, Geng Y, Gao C, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019.
Yanez LZ, Han J, Behr BB, Reijo Pera RA & Camarillo DB. Human oocyte developmental potential is predicted by mechanical properties within hours after fertilization. Nature Communications. 2016. 7 10809.
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2021-04-30
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