Cimadomo D, Capalbo A, Ubaldi FM, Scarica C, Palagiano A, Canipari R, et al. The impact of biopsy on human embryo developmental potential during preimplantation genetic diagnosis. Biomed Res Int. 2016;2016:1–10.

Article  Google Scholar 

Katz-Jaffe MG, Gardner DK. Embryology in the era of proteomics. Theriogenology. 2007;(68 Suppl 1):S125-30. https://doi.org/10.1016/j.theriogenology.2007.03.014.

Thouas GA, Dominguez F, Green MP, Vilella F, Simon C, Gardner DK. Soluble ligands and their receptors in human embryo development and implantation. Endocr Rev. 2015;36(1):92–130. https://doi.org/10.1210/er.2014-1042.

Article  CAS  PubMed  Google Scholar 

Krisher RL, Schoolcraft WB, Katz-Jaffe MG. Omics as a window to view embryo viability. Fertil Steril. 2015;103(2):333–41. https://doi.org/10.1016/j.fertnstert.2014.12.116.

Article  PubMed  Google Scholar 

Chen T-J, Zheng W-L, Liu C-H, Huang I, Lai H-H, Liu M. Using deep learning with large dataset of microscope images to develop an automated embryo grading system. Fertil Reprod. 2019;01:51–6.

Article  Google Scholar 

VerMilyea M, Hall JMM, Diakiw SM, Johnston A, Nguyen T, Perugini D, 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;35:770–84.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Valiuškaitė V, Raudonis V, Maskeliūnas R, Damaševičius R, Krilavičius T. Deep learning based evaluation of spermatozoid motility for artificial insemination. Sensors. 2020;21:72.

Article  PubMed  PubMed Central  Google Scholar 

Rad RM, Saeedi P, Au J, Havelock J. Trophectoderm segmentation in human embryo images via inceptioned U-Net. Med Image Anal. 2020;62:101612.

Article  PubMed  Google Scholar 

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;41:585–93.

Lee C-I, Su Y-R, Chen C-H, Chang TA, Kuo EE-S, Zheng W-L, et al. End-to-end deep learning for recognition of ploidy status using time-lapse videos. J Assist Reprod Genet. 2021;38:1655–63.

Article  PubMed  PubMed Central  Google Scholar 

Duval A, Nogueira D, Dissler N, Maskani Filali M, Delestro Matos F, Chansel-Debordeaux L, et al. A hybrid artificial intelligence model leverages multi-centric clinical data to improve fetal heart rate pregnancy prediction across time-lapse systems. Human Reprod. 2023;38(4):596–608. https://doi.org/10.1093/humrep/dead023.

Article  CAS  Google Scholar 

Wen J-Y, Liu C-F, Chung M-T, Tsai Y-C. Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET). Taiwan J Obstet Gynecol. 2022;61:837–46.

Article  PubMed  Google Scholar 

Khosravi P, Kazemi E, Zhan Q, Malmsten JE, Toschi M, Zisimopoulos P, et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med. 2019;2:21.

Article  PubMed  PubMed Central  Google Scholar 

Gingold JA, Ng NH, McAuley J, Lipton Z, Desai N. Predicting embryo morphokinetic annotations from time-lapse videos using convolutional neural networks. Fertil Steril. 2018;110:e220.

Article  Google Scholar 

Bamford T, Easter C, Montgomery S, Smith R, Dhillon-Smith RK, Barrie A, et al. A comparison of 12 machine learning models developed to predict ploidy, using a morphokinetic meta-dataset of 8147 embryos. Human Reprod. 2023;38(4):569–81. https://doi.org/10.1093/humrep/dead034.

Article  Google Scholar 

Barnes J, Brendel M, Gao VR, Rajendran S, Kim J, Li Q, et al. A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. Lancet Digit Health. 2023;5:e28-40.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Brendan McMahan H, Moore E, Ramage D, Hampson S, Agüera y Arcas B. Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 2017;54:1273–1282. Available from: https://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf.

Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021;27(10):1735–43. https://doi.org/10.1038/s41591-021-01506-3.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Roth HR, Chang K, Singh P, Neumark N, Li W, Gupta V, et al. Federated learning for breast density classification: a real-world implementation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2020. https://doi.org/10.1007/978-3-030-60548-3_18.

Lundberg SM, Lee S-I. A Unified approach to interpreting model predictions. In: Guyon I, Luxburg U Von, Bengio S, Wallach H, Fergus R, Vishwanathan S, et al., editors. Adv Neural Inf Process Syst [Internet]. Curran Associates, Inc.; 2017. Available from: https://proceedings.neurips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf. Accessed 30 Jul 2023.

Wu W, He L, Lin W, Mao R, Maple C, Jarvis S. SAFA: A semi-asynchronous protocol for fast federated learning with low overhead. IEEE Trans Comput. 2021;70. https://doi.org/10.1109/TC.2020.2994391.

Asad M, Moustafa A, Ito T. FedOpt: towards communication efficiency and privacy preservation in federated learning. Appl Sci (Switzerland). 2020;10. https://doi.org/10.3390/app10082864.

Chen Y, Sun X, Jin Y. Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation. IEEE Trans Neural Netw Learn Syst. 2020;31. https://doi.org/10.1109/TNNLS.2019.2953131.

Ren J, Yu G, Ding G. Accelerating DNN training in wireless federated edge learning systems. IEEE J Sel Areas Commun. 2021;39. https://doi.org/10.1109/JSAC.2020.3036971.

Li Q, Wu Z, Cai Y, Han Y, Yung CM, Fu T, et al. FedTree: a federated learning system for trees. Proc Mach Learn Syst 5 Pre-Proc. 2023. Available from: https://proceedings.mlsys.org/paper_files/paper/2023/file/3430e7055936cb8e26451ed49fce84a6-Paper-mlsys2023.pdf.

Xie C, Chen M, Chen P-Y, Li B. CRFL: certifiably robust federated learning against backdoor attacks. In: Meila M, Zhang T, editors. Proceedings of the 38th International Conference on Machine Learning [Internet]. PMLR; 2021. p. 11372–82. Available from: https://proceedings.mlr.press/v139/xie21a.html. Accessed 15 Aug 2023.

Mandal K, Gong G. PrivFL: Practical privacy-preserving federated regressions on high-dimensional data over mobile networks. Proceedings of the ACM Conference on Computer and Communications Security. 2019. https://doi.org/10.1145/3338466.3358926.

Chamikara MAP, Bertok P, Khalil I, Liu D, Camtepe S. Privacy preserving distributed machine learning with federated learning. Comput Commun. 2021;171. https://doi.org/10.1016/j.comcom.2021.02.014.

Acar A, Aksu H, Uluagac AS, Conti M. A survey on homomorphic encryption schemes. ACM Comput Surv. 2019;51:1–35.

Article  Google Scholar 

Dwork C, Roth A. The algorithmic foundations of differential privacy. Found Trends® Theor Comput Sci. 2013;9:211–407.

Article  Google Scholar 

Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, et al. Practical secure aggregation for federated learning on user-held data. 2016. https://doi.org/10.48550/arXiv.1611.04482.

Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, et al. Swarm Learning for decentralized and confidential clinical machine learning. Nature. 2021;594. https://doi.org/10.1038/s41586-021-03583-3.

Nguyen TV, Dakka MA, Diakiw SM, VerMilyea MD, Perugini M, Hall JMM, et al. A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data. Sci Rep. 2022;12:8888.

Article  CAS  PubMed  PubMed Central  Google Scholar