Total References=18
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Artificial Intelligence, Deep Learning and IVF+ - Course ID:67 -
Code
SEP143+
Comment
Explores the application of artificial intelligence in the management of clients and treatment cycles.
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Primary Topic 143
Date
23/08/2020 - [Last Updated:12/10/2020 10:06:24 AM]
Status
3
Contents
0 Activity ID
   
6 Key Topics
18 References
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References related to Artificial Intelligence, Deep Learning and IVF -[18]
Simopoulou M, Sfakianoudis K, Maziotis E, et al. Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence. J Assist Reprod Genet. 2018,35(9):1545-1557. doi:10.1007/s10815-018-1266-6
10.1007/s10815-018-1266-6 0 0 True Read article ID:2696
Wang R, Pan W, Jin L, et al. Artificial intelligence in reproductive medicine. Reproduction. 2019,158(4):R139-R154. doi:10.1530/REP-18-0523
10.1530/REP-18-0523 0 0 True Read article ID:2697
VerMilyea M, Hall JMM, Diakiw SM, et al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Hum Reprod. 2020,35(4):770-784. doi:10.1093/humrep/deaa013
10.1093/humrep/deaa013 0 0 True Read article ID:2698
Tran D, Cooke S, Illingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod. 2019,34(6):1011-1018. doi:10.1093/humrep/dez064
10.1093/humrep/dez064 0 0 True Read article ID:2699
Uyar A, Bener A, Ciray HN. Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods. Med Decis Making. 2015,35(6):714-725. doi:10.1177/0272989X14535984
10.1177/0272989X14535984 0 0 True Read article ID:2700
Zaninovic N, Elemento O, Rosenwaks Z. Artificial intelligence: its applications in reproductive medicine and the assisted reproductive technologies. Fertil Steril. 2019,112(1):28-30. doi:10.1016/j.fertnstert.2019.05.019
10.1016/j.fertnstert.2019.05.019 0 0 True Read article ID:2701
Ratna MB, Bhattacharya S, Abdulrahim B, McLernon DJ. A systematic review of the quality of clinical prediction models in in vitro fertilisation. Hum Reprod. 2020,35(1):100-116. doi:10.1093/humrep/dez258
10.1093/humrep/dez258 0 0 True Read article ID:2702
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019,110:12-22. doi:10.1016/j.jclinepi.2019.02.004
10.1016/j.jclinepi.2019.02.004 0 0 True Read article ID:2703
Raef B, Ferdousi R. A Review of Machine Learning Approaches in Assisted Reproductive Technologies. Acta Inform Med. 2019,27(3):205-211. doi:10.5455/aim.2019.27.205-211
10.5455/aim.2019.27.205-211 0 0 True Read article ID:2704
Letterie G. Mac Donald A. Artificial intelligence in IVF: a computer decision support system for day to day management of ovarian stimulation during in vitro fertilization. Fertil Steril. 2020, 114 (XXX–XX) https://doi.org/10.1016/j.fertnstert.2019.07.20
10.1016/j.fertnstert.2019.07.20 0 0 True Read article ID:2705
Chavez-Badiola A, Flores-Saiffe-Farías A, Mendizabal-Ruiz G, Drakeley AJ, Cohen J. Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reprod Biomed Online. 2020 Jul 5:S1472-6483(20)30373-4. doi: 10.1016/j.rbmo.2020.07.003
10.1016/j.rbmo.2020.07.003 0 8707 True Read article ID:2802
Curchoe CL. All Models Are Wrong, but Some Are Useful. J Assist Reprod Genet. 2020 Oct 7. doi: 10.1007/s10815-020-01895-3
10.1007/s10815-020-01895-3 366 8721 True Read article ID:2820
Curchoe CL, Bormann CL. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet. 2019 Apr,36(4):591-600. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6504989/
0 0 8722 True Read article ID:2821
Bormann CL, Kanakasabapathy MK, Thirumalaraju P, Gupta R, Pooniwala R, Kandula H, Hariton E, Souter I, Dimitriadis I, Ramirez LB, Curchoe CL, Swain J, Boehnlein LM, Shafiee H. Performance of a deep learning based neural network in the selection of human blastocysts for implantation. Elife. 2020 Sep 15,9:e55301. doi: 10.7554/eLife.55301
10.7554/eLife.55301 0 8723 True Read article ID:2822
Fernandez EI, Ferreira AS, Cecílio MHM, Chéles DS, de Souza RCM, Nogueira MFG, Rocha JC. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. J Assist Reprod Genet. 2020 Jul 11. doi: 10.1007/s10815-020-01881-9
10.1007/s10815-020-01881-9 0 8724 True Read article ID:2823
Tran D, Cooke S, Illingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod. 2019,34(6):1011-1018. doi:10.1093/humrep/dez064
10.1093/humrep/dez064 0 8252 True Read article ID:2693
Babayev E. Man versus machine in IVF-can artificial intelligence replace physicians? [published online ahead of print, 2020 Aug 17]. Fertil Steril. 2020,S0015-0282(20)30695-6. doi:10.1016/j.fertnstert.2020.07.042
10.1016/j.fertnstert.2020.07.042 0 8663 True Read article ID:2694
Jenkins J, van der Poel S, Krüssel J, et al. Empathetic application of machine learning may address appropriate utilization of ART [published online ahead of print, 2020 Jul 15]. Reprod Biomed Online. 2020,S1472-6483(20)30376-X. doi:10.1016/j.rbmo.2020.07.005
10.1016/j.rbmo.2020.07.005 0 8664 True Read article ID:2695

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