Keywords
artificial intelligence
oocytes
blastocyst
How to Cite
Abstract
Study question: Is there a correlation between the oocyte quality analyzed by MAGENTA ™ and the blastulation rate? Summary answer: Yes, the better quality of MAGENTA™, the higher blastulation rate. What is already known: We have embryonic classifications based on morphology but not a clinical application focused on classifying oocytes morphologically to determine their quality. MAGENTA™ is a system that allows each oocyte to be classified with a score to infer the probability of reaching blastocyst stage. Study design: Retrospective, comparative, transversal, observational study. 1,115 metaphase II (MII) oocytes were analyzed (2022 - 2024). Materials and Methods: Images were taken of each MII oocyte (prior to ICSI) and they were classified in real time using MAGENTA™ with a score from 1 to 10. Blastulation rate was analyzed (quality ≤ 3BB). The established groups were: group 1 (0 - 2.5); group 2 (2.6 - 5.5), group 3 (5.6 - 7.5) and group 4 (7.5 - 10). Statistics: ANOVA (Welch’s t). Results: There is a significant difference between the blastulation rate of group 1 (32%) compared to group 2 (53%) (p <0.0001). There is also a difference between group 2 (53%) and group 3 (60%) (p <0.05). However, there are no differences among group 3 (60%) and 4 (67%) (p<0.09). Implications of the findings: It allows for an objective classification of oocyte quality using a score. This implies having additional information to respond to treatments. Limitations of the study: The male factor is not considered.
References
Gardner, D.K., Balaban, B., 2016. Assessment of human embryo development using morphological criteria in an era of timelapse, algorithms and “OMICS”: is looking good still important? Mol Hum Reprod 22, 704–718. https://doi.org/10.1093/molehr/gaw057
Bormann, C.L., Thirumalaraju, P., Kanakasabapathy, M.K., Kandula, H., Souter, I., Dimitriadis, I., Gupta, R., Pooniwala, R., Shafiee, H., 2020. Consistency and objectivity of automated embryo assessments using deep neural networks. Fertil Steril 113, 781–787 https://doi.org/10.1016/j.fertnstert.2019.12.004
Fernandez, E.I., Ferreira, A.S., Cecílio, M.H.M., Cheles, D.S., de Souza, R.C.M., Nogueira, M.F.G., Rocha, J.C., 2020. 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 37, 2359–2376. https://doi.org/10.1007/s10815-020-01881-9
Xi, Q., Yang, Q., Wang, M., Huang, B., Zhang, B., Li, Z., Liu, S., Yang, L., Zhu, L., Jin, L., 2021. Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study. Reprod Biol Endocrinol 19, 53. https://doi.org/10.1186/s12958-021-00734-z
Coticchio, G., Behr, B., Campbell, A., Meseguer, M., Morbeck, D.E., Pisaturo, V., Plancha, C.E., Sakkas, D., Xu, Y., D’Hooghe, T., Cottell, E., Lundin, K., 2021. Fertility technologies and how to optimize laboratory performance to support the shortening of time to birth of a healthy singleton: a Delphi consensus. J Assist Reprod Genet 38, 1021–1043. https://doi.org/10.1007/s10815-021-02077-5
Letterie, G., MacDonald, A., 2020. Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertil Steril 114, 1026–1031. https://doi.org/10.1016/j.fertnstert.2020.06.006
Siristatidis, C., Vogiatzi, P., Pouliakis, A., Trivella, M., Papantoniou, N., Bettocchi, S., 2016. Predicting IVF Outcome: A Proposed Web-based System Using Artificial Intelligence. In Vivo 30, 507–512.
Rienzi, L., Vajta, G., Ubaldi, F., 2011. Predictive value of oocyte morphology in human IVF: a systematic review of the literature. Hum Reprod Update 17, 34–45. https://doi.org/10.1093/humupd/dmq029
Mercuri, N., Fjeldstad, J., Krivoi, A., Meriano, J., Nayot, D. A Non-Invasive, 2-Dimensional (2D) Image Analysis Artificial Intelligence (AI) Tool Scores Mature Oocytes And Correlates With The Quality Of Subsequent Blastocyst Development. ASRM Scientific Congress & Expo 2022. O-191, 11:45 AM Wednesday, October 26, 2022. Fertility & Sterility, Vol. 118, No. 4, Supplement, E78–79, October 2022.
Nayot D, Meriano J, Casper R, Krivoi A., 2020. An oocyte assessment tool using machine learning; Predicting blastocyst development based on a single image of an oocyte. 36th Annual Meeting of ESHRE – Copenhagen. https://futurefertility.com/wp-content/uploads/2022/11/ESHRE-2020-FF-Oocyte-assessment-tool-using-machine-learning-Predicting-blastocyst-development-based-on-oocyte-image.pdf
Fjeldstad, J., Qi, W., Mercuri, N., Siddique, N., Meriano, J., Krivoi, A., Nayot, D. An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes. Reproductive BioMedicine Online. 17 de enero de 2024.
Weghofer, A., Munne, S., Chen, S., Barad, D., Gleicher, N., 2007. Lack of association between polycystic ovary syndrome and embryonic aneuploidy. Fertility and Sterility 88, 900–905. https://doi.org/10.1016/j.fertnstert.2006.12.018
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2025 AJ Aon, V Julianelli, J Fjeldstad, N Mercuri; M Geller; M Lavolpe