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Key Points
Artificial Intelligence, Deep Learning and IVF+ - Course ID:67 -
Code
 SEP143+ [832]
Comment
 Explores the application of artificial intelligence in the management of clients and treatment cycles.
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Primary Topic 143
Date
 7/1/2021 - [Last Updated:8/12/2020]
Status
 3
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Activity Date
Created 7/1/2021
Updated 8/12/2020
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Contents
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6 Key Topics
1 Comments
0 Questions
20 References
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0 CPD Minutes
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References related to Artificial Intelligence, Deep Learning and IVF -[19] [Ref=20]
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 ID:2693
Year:0, DOI:10.1093/humrep/dez064 0 8252 Last edited on
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 ID:2694
Year:0, DOI:10.1016/j.fertnstert.2020.07.042 0 8663 Last edited on
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 ID:2695
Year:0, DOI:10.1016/j.rbmo.2020.07.005 0 8664 Last edited on
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 ID:2696
Year:0, DOI:10.1007/s10815-018-1266-6 0 0 Last edited on
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 ID:2697
Year:0, DOI:10.1530/REP-18-0523 0 0 Last edited on
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 ID:2698
Year:0, DOI:10.1093/humrep/deaa013 0 0 Last edited on
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 ID:2699
Year:0, DOI:10.1093/humrep/dez064 0 0 Last edited on
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 ID:2700
Year:0, DOI:10.1177/0272989X14535984 0 0 Last edited on
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 ID:2701
Year:0, DOI:10.1016/j.fertnstert.2019.05.019 0 0 Last edited on
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 ID:2702
Year:0, DOI:10.1093/humrep/dez258 0 0 Last edited on
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 ID:2703
Year:0, DOI:10.1016/j.jclinepi.2019.02.004 0 0 Last edited on
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 ID:2704
Year:0, DOI:10.5455/aim.2019.27.205-211 0 0 Last edited on
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 ID:2705
Year:0, DOI:10.1016/j.fertnstert.2019.07.20 0 0 Last edited on
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 ID:2802
Year:0, DOI:10.1016/j.rbmo.2020.07.003 0 8707 Last edited on
Curchoe CL. All Models Are Wrong, but Some Are Useful. J Assist Reprod Genet. 2020 Oct 7. doi: 10.1007/s10815-020-01895-3 ID:2820
Year:0, DOI:10.1007/s10815-020-01895-3 366 8721 Last edited on
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/ ID:2821
Year:0, DOI:0 0 8722 Last edited on
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 ID:2822
Year:0, DOI:10.7554/eLife.55301 0 8723 Last edited on
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 ID:2823
Year:0, DOI:10.1007/s10815-020-01881-9 0 8724 Last edited on
Bori L, Dominguez F, Fernandez EI, Del Gallego R, Alegre L, Hickman C, Quiñonero A, Nogueira MFG, Rocha JC, Meseguer M. An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study. Reprod Biomed Online. 2020 Oct 8:S1472-6483(20)30537-X. doi: 10.1016/j.rbmo.2020.09.031 ID:2948
Year:2020, DOI:10.1016/j.rbmo.2020.09.031 0 8788 2020 Last edited on 8/12/2020
Bori L, Paya E, Alegre L, Viloria TA, Remohi JA, Naranjo V, Meseguer M. Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Fertil Steril. 2020 Dec,114(6):1232-1241. doi: 10.1016/j.fertnstert.2020.08.023 ID:2949
Year:2020, DOI:10.1016/j.fertnstert.2020.08.023 0 0 2020 Last edited on 8/12/2020
Additional References related to Artificial Intelligence, Deep Learning and IVF - [1]
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