Deep Learning Methods to Automate Embryo Classification and Evaluation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34222-6_1
Reference25 articles.
1. Kushnir, V.A., Smith, G.D., Adashi, E.Y. The Future of IVF: The New Normal in Human Reproduction. Reprod. Sci. 29, 849–856 (2022). https://doi.org/10.1007/s43032-021-00829-3
2. Choucair, F., Younis, N., Hourani, A.: The value of the modern embryologist to a successful IVF system: revisiting an age-old question. Middle East Fertility Society J. 26(1), 1–6 (2021). https://doi.org/10.1186/s43043-021-00061-8
3. Bormann CL, et al.: Performance of a deep learning based neural network in the selection of human blastocysts for implantation. Elife. 2020 Sep 15;9:e55301. PMID: 32930094; PMCID: PMC7527234. https://doi.org/10.7554/eLife.55301
4. Amitai, T., Kan-Tor, Y., Srebnik, N., Buxboim, A.: Embryo classification beyond pregnancy: early prediction of first trimester miscarriage using machine learning medRxiv 11(24), 20237610 (2020). https://doi.org/10.1101/2020.11.24.20237610
5. Wu, Y.G., et al.: Different effectiveness of closed embryo culture system with time-lapse imaging (EmbryoScope(TM)) in comparison to standard manual embryology in good and poor prognosis patients: a prospectively randomized pilot study. Reprod Biol Endocrinol. 2016 Aug 24; 14(1):49. PMID: 27553622; PMCID: PMC4995783. https://doi.org/10.1186/s12958-016-0181-x
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