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Influence of oocyte and sperm parameters on the morphological quality and the aneuploidy status of the ICSI embryos: a mathematical modelling approach

Published online by Cambridge University Press:  19 August 2025

Hakan Aytacoglu*
Affiliation:
Department of Medical Genetics, Near East University, Nicosia, Cyprus
David Amilo
Affiliation:
Department of Mathematics, Near East University, Nicosia, Cyprus Near East University, Mathematics Research Centre, Nicosia, Cyprus
Bilgen Kaymakamzade
Affiliation:
Department of Mathematics, Near East University, Nicosia, Cyprus Near East University, Mathematics Research Centre, Nicosia, Cyprus
Evren Hincal
Affiliation:
Department of Mathematics, Near East University, Nicosia, Cyprus Near East University, Mathematics Research Centre, Nicosia, Cyprus
Onder Coban
Affiliation:
British IVF Centre, Nicosia, Cyprus
Pinar Tulay
Affiliation:
Department of Medical Genetics, Near East University, Nicosia, Cyprus Near East University, DESAM Research Institute, Nicosia, Cyprus
*
Corresponding author: Hakan Aytacoglu; Email: hakan.aytacoglu@neu.edu.tr

Summary

Recently, mathematical and computational approaches have been incorporated into ICSI interventions as guiding tools. However, those tools carry no prognostic potential. Improving this capability may enhance ICSI attempts and assist clinicians working in infertility clinics. This study, thus aimed to investigate whether parental parameters could have predictive potential for the quality of resulting embryos with the ICSI approach using mathematical modelling techniques. Patient data including follicle number, MI and MII oocyte numbers, sperm number, sperm morphology and motility for 765 distinct couples attending British Cyprus IVF hospital was collected. Furthermore, morphological quality data as well as aneuploidy status for the 4123 resultant embryos were obtained. Regression analyses were conducted to observe the possible correlations between parental parameters and embryo quality and ploidy. Correlation analyses showed that follicle and oocyte numbers, as well as sperm parameters can be indicative of morphological quality of resulting embryos via ICSI (p values < 0.05). On the other hand, aneuploidy prediction remains too complicated to be predicted solely by these parameters (p values > 0.05). This study indicates a predictive potential of infertility measurements for male and female partners on ICSI success and is expected to act as a basis for the development of prognostic softwares to be used in IVF clinics.

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Type
Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Abdala, A., Elkhatib, I., Bayram, A., Arnanz, A., El-Damen, A., Melado, L., Lawrenz, B., Fatemi, H.M. and De Munck, N. (2022) Day 5 versus day 6 single euploid blastocyst frozen embryo transfers: which variables do have an impact on the clinical pregnancy rates? Journal of Assisted Reproduction and Genetics 39(2), 379. https://doi.org/10.1007/S10815-021-02380-1.CrossRefGoogle Scholar
Barad, D.H., Albertini, D.F., Molinari, E. and Gleicher, N. (2022) IVF outcomes of embryos with abnormal PGT-A biopsy previously refused transfer: a prospective cohort study. Human Reproduction 37(6), 11941206. https://doi.org/10.1093/HUMREP/DEAC063.CrossRefGoogle ScholarPubMed
Barnes, J., Brendel, M., Gao, V.R., Rajendran, S., Kim, J., Li, Q., Malmsten, J.E., Sierra, J.T., Zisimopoulos, P., Sigaras, A. and Khosravi, P. (2023) A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. The Lancet Digital Health 5(1), e28e40. https://doi.org/10.1016/S2589-7500(22)00213-8.CrossRefGoogle ScholarPubMed
Brugo-Olmedo, S., Chillik, C. and Kopelman, S. (2001) Definition and causes of infertility. Reproductive BioMedicine Online 2(1), 173185. https://doi.org/10.1016/S1472-6483(10)62193-1.CrossRefGoogle ScholarPubMed
Cai, Y., Ding, M., Lin, F., Diao, Z., Zhang, N., Sun, H. and Zhou, J. (2019) Evaluation of preimplantation genetic testing based on next-generation sequencing for balanced reciprocal translocation carriers. Reproductive BioMedicine Online 38(5), 669675. https://doi.org/10.1016/J.RBMO.2018.12.043.CrossRefGoogle ScholarPubMed
Capalbo, A., Rienzi, L., Cimadomo, D., Maggiulli, R., Elliott, T., Wright, G., Nagy, Z.P. and Ubaldi, F.M. (2014) Correlation between standard blastocyst morphology, euploidy and implantation: an observational study in two centers involving 956 screened blastocysts. Human Reproduction 29(6), 11731181. https://doi.org/10.1093/HUMREP/DEU033.CrossRefGoogle ScholarPubMed
Carrell, D.T., Wilcox, A.L., Lowy, L., Peterson, C.M., Jones, K.P., Erickson, L., Campbell, B., Branch, D.W. and Hatasaka, H.H. (2003) Elevated sperm chromosome aneuploidy and apoptosis in patients with unexplained recurrent pregnancy loss. Obstetrics & Gynecology 101(6), 12291235. https://doi.org/10.1016/S0029-7844(03)00339-9.Google ScholarPubMed
Carson, S.A. and Kallen, A.N. (2021) Diagnosis and management of infertility: a review. JAMA 326(1), 6576. https://doi.org/10.1001/JAMA.2021.4788.CrossRefGoogle ScholarPubMed
Cecchele, A., Cermisoni, G.C., Giacomini, E., Pinna, M. and Vigano, P. (2022) Cellular and molecular nature of fragmentation of human embryos. International Journal of Molecular Sciences 23(3), 1349. https://doi.org/10.3390/IJMS23031349.CrossRefGoogle ScholarPubMed
Chavez-Badiola, A., Flores-Saiffe-Farías, A., Mendizabal-Ruiz, G., Drakeley, A.J. and Cohen, J. (2020) Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reproductive Biomed Online 41(4), 585593. https://doi.org/10.1016/J.RBMO.2020.07.003.CrossRefGoogle ScholarPubMed
Check, J.H., Graziano, V., Cohen, R., Krotec, J. and Check, M.L. (2009) Effect of an abnormal sperm chromatin structural assay (SCSA) on pregnancy outcome following (IVF) with ICSI in previous IVF failures. Archives of Andrology 51(2), 121124. https://doi.org/10.1080/014850190518125.CrossRefGoogle Scholar
Ciray, H.N., Aksoy, T., Goktas, C., Ozturk, B. and Bahceci, M. (2012) Time-lapse evaluation of human embryo development in single versus sequential culture media-a sibling oocyte study. Journal of Assisted Reproduction and Genetics 29(9), 891900. https://doi.org/10.1007/S10815-012-9818-7/TABLES/5.CrossRefGoogle ScholarPubMed
Coates, A, Coate, B., Holmes, L. and Griffin, D. (2015) Morphological and kinetic embryological criteria and correlation with aneuploidy rates: how might they be used to choose the best IVF embryo for transfer? Human Genetics & Embryology 5(3), 129. https://doi.org/10.4172/2161-0436.1000129.Google Scholar
Coates, A., Kung, A., Mounts, E., Hesla, J., Bankowski, B., Barbieri, E., Ata, B., Cohen, J. and Munné, S. (2017) Optimal euploid embryo transfer strategy, fresh versus frozen, after preimplantation genetic screening with next generation sequencing: a randomized controlled trial. Fertility and Sterility 107(3), 723730.e3. https://doi.org/10.1016/J.FERTNSTERT.2016.12.022.CrossRefGoogle ScholarPubMed
Coban, O., Serdarogullari, M., Yarkiner, Z. and Serakinci, N. (2020) Investigating the level of DNA double-strand break in human spermatozoa and its relation to semen characteristics and IVF outcome using phospho-histone H2AX antibody as a biomarker. Andrology 8(2), 421426. https://doi.org/10.1111/ANDR.12689.CrossRefGoogle Scholar
Cornelisse, S., Zagers, M., Kostova, E., Fleischer, K., van Wely, M. and Mastenbroek, S. (2020) Preimplantation genetic testing for aneuploidies (abnormal number of chromosomes) in in vitro fertilisation. Cochrane Database of Systematic Reviews 9(9). https://doi.org/10.1002/14651858.CD005291.pub3.Google ScholarPubMed
Doroftei, B., Ilie, O.D., Anton, N., Armeanu, T. and Ilea, C. (2022) A mini-review regarding the clinical outcomes of In Vitro Fertilization (IVF) following Pre-Implantation Genetic Testing (PGT)-Next Generation Sequencing (NGS) approach. Diagnostics 12(8), 1911. https://doi.org/10.3390/DIAGNOSTICS12081911.CrossRefGoogle ScholarPubMed
Fischer-Holzhausen, S. and Röblitz, S. (2022) Mathematical modelling of follicular growth and ovarian stimulation. Current Opinion in Endocrine and Metabolic Research 26, 100385. https://doi.org/10.1016/J.COEMR.2022.100385.CrossRefGoogle Scholar
Fishel, S., Gordon, A., Lynch, C., Dowell, K., Ndukwe, G., Kelada, E., Thornton, S., Jenner, L., Cater, E., Brown, A. and Garcia-Bernardo, J. (2010) Live birth after polar body array comparative genomic hybridization prediction of embryo ploidy—the future of IVF? Fertility and Sterility 93(3), 1006.e71006.e10. https://doi.org/10.1016/J.FERTNSTERT.2009.09.055.CrossRefGoogle ScholarPubMed
Fortin, C.S., Leader, A., Mahutte, N., Hamilton, S., Léveillé, M.C., Villeneuve, M. and Sirard, M.A. (2019) Gene expression analysis of follicular cells revealed inflammation as a potential IVF failure cause. Journal of Assisted Reproduction and Genetics 36(6), 11951210. https://doi.org/10.1007/S10815-019-01447-4/FIGURES/2.CrossRefGoogle ScholarPubMed
Gardner, D.K., Meseguer, M., Rubio, C. and Treff, N.R. (2015) Diagnosis of human preimplantation embryo viability. Human Reproduction Update 21(6), 727747. https://doi.org/10.1093/HUMUPD/DMU064.CrossRefGoogle ScholarPubMed
Gardner, D.K. and Schoolcraft, W.B. (1999) Culture and transfer of human blastocysts. Current Opinion in Obstetrics and Gynecology 11(3), 307311.10.1097/00001703-199906000-00013CrossRefGoogle ScholarPubMed
Gulersen, M., Baum, S., Bornstein, E., Krantz, D., Singer, T. and Divon, M.Y. (2019) The impact of preimplantation genetic testing on prenatal diagnostic procedures. The Journal of Maternal-Fetal & Neonatal Medicine 34(18), 30663069. https://doi.org/10.1080/14767058.2019.1677598.CrossRefGoogle ScholarPubMed
Hassan, M.R., Al-Insaif, S., Hossain, M.I. and Kamruzzaman, J. (2020) A machine learning approach for prediction of pregnancy outcome following IVF treatment. Neural Computing and Applications 32(7), 22832297. https://doi.org/10.1007/S00521-018-3693-9/TABLES/7.CrossRefGoogle Scholar
Hou, W., Xu, Y., Li, R., Song, J., Wang, J., Zeng, Y., Pan, J., Zhou, C. and Xu, Y. (2019) Role of aneuploidy screening in preimplantation genetic testing for monogenic diseases in young women. Fertility and Sterility 111(5), 928935. https://doi.org/10.1016/J.FERTNSTERT.2019.01.017.CrossRefGoogle ScholarPubMed
Huang, T.T., Huang, D.H., Ahn, H.J., Arnett, C. and Huang, C.T. (2019) Early blastocyst expansion in euploid and aneuploid human embryos: evidence for a non-invasive and quantitative marker for embryo selection. Reproductive Biomed Online 39(1), 2739. https://doi.org/10.1016/J.RBMO.2019.01.010.CrossRefGoogle ScholarPubMed
Janny, L. and Menezo, Y.J.R. (1996) Maternal age effect on early human embryonic development and blastocyst formation. Molecular Reproduction and Development 45, 3137. https://doi.org/10.1002/(SICI)1098-2795(199609)45:1.3.0.CO;2-T>CrossRefGoogle ScholarPubMed
Kilani, S. and Chapman, M.G. (2014) Meiotic spindle normality predicts live birth in patients with recurrent in vitro fertilization failure. Fertility and Sterility 101(2), 403406.e1. https://doi.org/10.1016/j.fertnstert.2013.10.045.CrossRefGoogle ScholarPubMed
Kimelman, D. and Pavone, M.E. (2021) Non-invasive prenatal testing in the context of IVF and PGT-A. Best Practice & Research Clinical Obstetrics & Gynaecology 70, 5162. https://doi.org/10.1016/J.BPOBGYN.2020.07.004.CrossRefGoogle ScholarPubMed
Kroener, L.L., Ambartsumyan, G., Pisarska, M.D., Briton-Jones, C., Surrey, M. and Hill, D. (2015) Increased blastomere number in cleavage-stage embryos is associated with higher aneuploidy. Fertility and Sterility 103(3), 694698. https://doi.org/10.1016/J.FERTNSTERT.2014.12.090.CrossRefGoogle ScholarPubMed
Kromp, F., Balaban, B., Cottin, V., Saiz, I.C., Fancsovits, P., Fawzy, M., Findikli, N., Kovacic, B., Ljiljak, D., Rodero, I.M. and Parmegiani, L. (2022) O-285 Artificial intelligence algorithms reach expert-level accuracy in automated grading of blastocyst morphology assessment based on static embryo images and Gardner criteria. Human Reproduction 37(1), deac106-078. https://doi.org/10.1093/HUMREP/DEAC106.078.CrossRefGoogle Scholar
Kruger, T.F., Menkveld, R., Stander, F.S.H., Lombard, C.J., Merwe, J.P., Van der Zyl, J. A. and Smith, K. (1986) Sperm morphologic features as a prognostic factor in in vitro fertilization. Fertility and Sterility 46(6), 11181123. https://doi.org/10.1016/S0015-0282(16)49891-2.CrossRefGoogle ScholarPubMed
Kushnir, V.A., Barad, D.H., Albertini, D.F., Darmon, S.K. and Gleicher, N. (2017) Systematic review of worldwide trends in assisted reproductive technology 2004-2013. Reproductive Biology and Endocrinology: RB&E 15(1), 6. https://doi.org/10.1186/S12958-016-0225-2.CrossRefGoogle ScholarPubMed
Lagalla, C., Coticchio, G., Sciajno, R., Tarozzi, N., Zacà, C. and Borini, A. (2020) Alternative patterns of partial embryo compaction: prevalence, morphokinetic history and possible implications. Reproductive Biomed Online 40(3), 347354. https://doi.org/10.1016/J.RBMO.2019.11.011.CrossRefGoogle ScholarPubMed
Manna, C., Nanni, L., Lumini, A. and Pappalardo, S. (2013) Artificial intelligence techniques for embryo and oocyte classification. Reproductive Biomed Online 26(1), 4249. https://doi.org/10.1016/J.RBMO.2012.09.015.CrossRefGoogle ScholarPubMed
Miyara, F., Aubriot, F.X., Glissant, A., Nathan, C., Douard, S., Stanovici, A., Herve, F., Dumont-Hassan, M., Meur, A.L., Cohen-Bacrie, P. and Debey, P. (2003) Multiparameter analysis of human oocytes at metaphase II stage after IVF failure in non-male infertility. Human Reproduction 18(7), 14941503. https://doi.org/10.1093/HUMREP/DEG272.CrossRefGoogle ScholarPubMed
Pagliardini, L., Viganò, P., Alteri, A., Corti, L., Somigliana, E. and Papaleo, E. (2020) Shooting STAR: reinterpreting the data from the ‘Single Embryo TrAnsfeR of Euploid Embryo’ randomized clinical trial. Reproductive Biomed Online 40(4), 475478. https://doi.org/10.1016/J.RBMO.2020.01.015.CrossRefGoogle ScholarPubMed
Pasternak, M., Thompson, M., Rosenwaks, Z. and Spandorfer, S. (2018) Embryo morphology on day 3 of embryogenesis is predictive of aneuploidy in genetically tested embryos. Fertility and Sterility 110(4), e348. https://doi.org/10.1016/j.fertnstert.2018.07.971.CrossRefGoogle Scholar
Penzias, A.S. (2012) Recurrent IVF failure: other factors. Fertility and Sterility 97(5), 10331038. https://doi.org/10.1016/J.FERTNSTERT.2012.03.017.CrossRefGoogle ScholarPubMed
Pons, M.C., Carrasco, B., Parriego, M., Boada, M., González-Foruria, I., Garcia, S., Coroleu, B., Barri, P.N. and Veiga, A. (2019) Deconstructing the myth of poor prognosis for fast-cleaving embryos on day 3. Is it time to change the consensus? Journal of Assisted Reproduction and Genetics 36(11), 22992305. https://doi.org/10.1007/S10815-019-01574-Y.CrossRefGoogle Scholar
Puscheck, E.E. and Jeyendran, R.S. (2007) The impact of male factor on recurrent pregnancy loss. Current Opinion in Obstetrics and Gynecology 19(3), 222228. https://doi.org/10.1097/GCO.0B013E32813E3FF0.CrossRefGoogle ScholarPubMed
Qiao, J., Wang, Z.-B., Feng, H.-L., Miao, Y.-L., Wang, Q., Yu, Y., Wei, Y.-C., Yan, J., Wang, W.H., Shen, W. and Sun, S.C. (2014) The root of reduced fertility in aged women and possible therapeutic options: current status and future prospects. Molecular Aspects of Medicine 38, 5485. https://doi.org/10.1016/j.mam.2013.06.001.CrossRefGoogle Scholar
Sadeghi, M.R. (2018) The 40th anniversary of IVF: has ART’s success reached its peak? Journal of Reproduction & Infertility 19(2), 67.Google ScholarPubMed
Salame, A.A., Dahdouh, E.M., Aljafari, R., Samuel, D.A., Koodathingal, B.P., Bajpai, A., Kainoth, S. and Fakih, M. (2024) Predictive factors of aneuploidy in infertile patients undergoing IVF: a retrospective analysis in a private IVF practice. Middle East Fertility Society Journal 29(1), 110. https://doi.org/10.1186/S43043-024-00172-Y/TABLES/5.CrossRefGoogle Scholar
Simopoulou, M., Sfakianoudis, K., Antoniou, N., Maziotis, E., Rapani, A., Bakas, P., Anifandis, G., Kalampokas, T., Bolaris, S., Pantou, A. and Pantos, K. (2018a) Making IVF more effective through the evolution of prediction models: is prognosis the missing piece of the puzzle? Systems Biology in Reproductive Medicine 64(5), 305323. https://doi.org/10.1080/19396368.2018.1504347.CrossRefGoogle ScholarPubMed
Simopoulou, M., Sfakianoudis, K., Maziotis, E., Antoniou, N., Rapani, A., Anifandis, G., Bakas, P., Bolaris, S., Pantou, A., Pantos, K. and Koutsilieris, M. (2018b) Are computational applications the ‘crystal ball’ in the IVF laboratory? The evolution from mathematics to artificial intelligence. Journal of Assisted Reproduction and Genetics 35(9), 15451557. https://doi.org/10.1007/S10815-018-1266-6/FIGURES/2.CrossRefGoogle ScholarPubMed
Thang, L.D., Thuy, N.M., Dung, T.C., Anh, P.T.T., Quy, P.N., Ngoc, V.T., Linh, H.M., Le Thuy, N., Anh, C.T., Thuy, T.T., Huong, N.T., Hoang, L. and Hugues, J.N. (2024) The impact of embryo quality on pregnancy outcomes in single day 5 versus day 6 euploid blastocyst transfer: a retrospective cohort study. International Journal of Fertility & Sterility 18(3), 228. https://doi.org/10.22074/IJFS.2023.2006100.1488.Google ScholarPubMed
Thompson, C. (2016) IVF global histories, USA: between rock and a marketplace. Reproductive Biomedicine & Society Online 2, 128135. https://doi.org/10.1016/J.RBMS.2016.09.003.CrossRefGoogle Scholar
Tiegs, A.W., Sun, L., Patounakis, G. and Scott, R.T. (2019) Worth the wait? Day 7 blastocysts have lower euploidy rates but similar sustained implantation rates as Day 5 and Day 6 blastocysts. Human Reproduction 34(9), 16321639. https://doi.org/10.1093/HUMREP/DEZ138.CrossRefGoogle Scholar
Tilia, L., Chapman, M., Kilani, S., Cooke, S. and Venetis, C. (2020) Oocyte meiotic spindle morphology is a predictive marker of blastocyst ploidy-a prospective cohort study. Fertility and Sterility 113(1), 105113e.1. https://doi.org/10.1016/j.fertnstert.2019.08.070.CrossRefGoogle ScholarPubMed
Tong, J., Niu, Y., Wan, A. and Zhang, T. (2022) Effect of parental origin and predictors for obtaining a euploid embryo in balanced translocation carriers. Reprod Biomed Online 44(1), 7279. https://doi.org/10.1016/J.RBMO.2021.09.007.CrossRefGoogle ScholarPubMed
Treff, N.R., Eccles, J., Lello, L., Bechor, E., Hsu, J., Plunkett, K., Zimmerman, R., Rana, B., Samoilenko, A., Hsu, S. and Tellier, L.C. (2019) Utility and first clinical application of screening embryos for polygenic disease risk reduction. Frontiers in Endocrinology 10, 485071. https://doi.org/10.3389/FENDO.2019.00845/BIBTEX.CrossRefGoogle ScholarPubMed
Uyar, A., Torrealday, S. and Seli, E. (2013) Cumulus and granulosa cell markers of oocyte and embryo quality. Fertility and Sterility 99(4), 979997. https://doi.org/10.1016/J.FERTNSTERT.2013.01.129.CrossRefGoogle ScholarPubMed
Wang, J., Diao, Z., Fang, J., Zhu, L., Xu, Z., Lin, F., Zhang, N. and Chen, L. (2022) The influence of day 3 embryo cell number on the clinical pregnancy and live birth rates of day 5 single blastocyst transfer from frozen embryo transfer cycles. BMC Pregnancy and Childbirth 22(1), 980. https://doi.org/10.1186/S12884-022-05337-Z.CrossRefGoogle ScholarPubMed
Wells, D. (2010) Embryo aneuploidy and the role of morphological and genetic screening. Reproductive Biomedicine Online 21, 274277. https://doi.org/10.1016/j.rbmo.2010.06.035.CrossRefGoogle Scholar
Wong, K.M., Mastenbroek, S. and Repping, S. (2014) Cryopreservation of human embryos and its contribution to in vitro fertilization success rates. Fertility and Sterility 102(1), 1926. https://doi.org/10.1016/J.FERTNSTERT.2014.05.027.CrossRefGoogle ScholarPubMed
World Health Organisation. (2023) Infertility Prevalence Estimates, 1990–2021. Geneva: World Health Organization.Google Scholar
World Health Organisation. (2021) World Health Organization. WHO Laboratory Manual for the Examination and Processing of Human Semen. 6th ed. (H. World Health Organization, Ed.) WHO Press 276. World Health Organization, Department of Reproductive Health and Research. Available at https://www.who.int/publications/i/item/9789240030787 (accessed July 2023).Google Scholar
Yan, J., Qin, Y., Zhao, H., Sun, Y., Gong, F., Li, R., Sun, X., Ling, X., Li, H., Hao, C. and Tan, J. (2021) live birth with or without preimplantation genetic testing for aneuploidy. New England Journal of Medicine 385(22), 20472058. https://doi.org/10.1056/NEJMOA2103613.CrossRefGoogle ScholarPubMed
Yenkie, K.M., Diwekar, U.M. and Bhalerao, V. (2013) Modeling the superovulation stage in in vitro fertilization. IEEE Transactions on Biomedical Engineering 60(11), 30033008. https://doi.org/10.1109/TBME.2012.2227742.CrossRefGoogle ScholarPubMed
Zegers-Hochschild, F., Adamson, G.D., Dyer, S., Racowsky, C., Mouzon, J., Sokol, R. and Rienzi, L. (2017) The International Glossary on Infertility and Fertility Care, 2017. Fertility and Sterility 108(3), 393406. https://doi.org/10.1016/J.FERTNSTERT.2017.06.005.CrossRefGoogle ScholarPubMed
Zhan, Q., Sierra, E.T., Malmsten, J., Ye, Z., Rosenwaks, Z. and Zaninovic, N. (2020) Blastocyst score, a blastocyst quality ranking tool, is a predictor of blastocyst ploidy and implantation potential. F S Reports 1(2), 133141. https://doi.org/10.1016/J.XFRE.2020.05.004.CrossRefGoogle Scholar