Evaluation of embryonic ploidy

DOI

https://dx.doi.org/10.18565/aig.2025.106

Yashchuk A.G., Gromenko D.D., Nasyrova S.F., Gromenko Iu.Iu.
1) Bashkir State Medical University, Ministry of Health of the Russian Federation, Ufa, Russia; 2) Medical Center "Family", Ufa, Russia

Embryo aneuploidy is a leading cause of implantation failure and miscarriage during early pregnancy. Preimplantation genetic testing for aneuploidies (PGT-A) enables the assessment of embryo ploidy before transfer; however, it has several limitations. The integration of automated analysis algorithms into embryologists' workflows can significantly enhance embryo selection and mitigate human errors.
Objective: To evaluate the effectiveness of automated analysis algorithms in determining embryo ploidy across different age groups.
Materials and methods: This retrospective study was conducted from January to May 2022 at the Family Medical Center and included embryos from 51 patients who underwent in vitro fertilization (IVF) with PGT-A. The effectiveness of determining euploidy based on blastocyst images was compared with the results obtained through PGT-A. The study utilized the Embryo Ranking Intelligent Classification Algorithm (ERICA 1.0) software.
Results: A total of 117 blastocysts were obtained, of which 101 were subjected to PGT-A and automated analysis: 31 blastocysts from women under 35 years of age (mean age 30.7 years), 39 blastocysts from women aged 35–39 years (mean age 37.4 years), and 31 blastocysts from women over 40 years of age (mean age 42 years). According to the PGT-A results for 101 embryos, the euploidy rate was 51.5%. The accuracy, positive predictive value, negative predictive value, sensitivity, specificity, and area under the ROC curve were 0.74, 0.76, 0.73, 0.73, 0.76, and 0.78, respectively. The most significant results were observed in patients aged < 35 years.
Conclusion: Automated image analysis shows promise as an auxiliary tool for decision-making in embryo selection, particularly in patients over 35 years of age.

Authors' contributions: Yashchuk A.G., Nasyrova S.F. – conception and design of the study, editing of the manuscript; Gromenko Iu.Iu. – data collection; Gromenko D.D. –statistical analysis, drafting of the manuscript.
Conflicts of interest: The authors have no conflicts of interest to declare.
Funding: There was no funding for this study.
Acknowledgment: The authors express their gratitude to Dr. Alejandro Chavez-Badiola and his team for providing consent to use the ERICA 1.0 AI in this study.
Ethical Approval: The study was reviewed and approved by the Research Ethics Committee of the institution.
Patient Consent for Publication: All patients provided informed consent for the publication of their data.
Authors' Data Sharing Statement: The data supporting the findings of this study are available upon request from the corresponding author after approval from the principal investigator.
For citation: Yashchuk A.G., Gromenko D.D., Nasyrova S.F., Gromenko Iu.Iu. Evaluation of embryonic ploidy.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2025; (9): 126-132 (in Russian)
https://dx.doi.org/10.18565/aig.2025.106

artificial intelligence

euploid embryo

preimplantation genetic testing for aneuploidies (PGT-A)

late reproductive age

Full text (in Russian)

Melo P., Dhillon-Smith R., Islam M.A., Devall A., Coomarasamy A. Genetic causes of sporadic and recurrent miscarriage. Fertil. Steril. 2023; 120(5): 940-4. https://dx.doi.org/10.1016/j.fertnstert.2023.08.952
Matorras R., Pérez-Fernández S., Mercader A., Sierra S., Larreategui Z., Ferrando M. et al. Lessons learned from 64,071 embryos subjected to PGT for aneuploidies: results, recurrence pattern and indications analysis. Reprod. Biomed. Online. 2024; 49(5): 103979. https://dx.doi.org/10.1016/j.rbmo.2024.103979
Российская Aссоциация Репродукции Человека. Регистр ВРТ. Отчет за 2022 год. Доступно по: https://www.rahr.ru/d_registr_otchet/RegistrVRT_2022.pdf [Russian Association of Human Reproduction. ART Register. 2022 Report. Available at: https://www.rahr.ru/d_registr_otchet/RegistrVRT_2022.pdf (in Russian)].
Casper R.F. PGT-A: Houston, we have a problem. J. Assist. Reprod. Genet. 2023; 40(10): 2325-32. https://dx.doi.org/10.1007/s10815-023-02913-w
Theobald R., SenGupta S., Harper J. The status of preimplantation genetic testing in the UK and USA. Hum. Reprod. 2020; 35(4): 986-98. https://dx.doi.org/10.1093/humrep/deaa034
Roos Kulmann M.I., Lumertz Martello C., Bos-Mikich A., Frantz N. Pronuclear and blastocyst morphology are associated age-dependently with embryo ploidy in in vitro fertilization cycles. Hum. Fertil. (Camb). 2022; 25(2): 369-76. https://dx.doi.org/10.1080/14647273.2020.1808716
Khosravi P., Kazemi E., Zhan Q., Malmsten J.E., 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. https://dx.doi.org/10.1038/s41746-019-0096-y
Cimadomo D., Rienzi L., Conforti A., Forman E., Canosa S., Innocenti F. et al. Opening the black box: why do euploid blastocysts fail to implant? A systematic review and meta-analysis. Hum. Reprod. Update. 2023; 29(5): 570-633. https://dx.doi.org/10.1093/humupd/dmad010
Giménez C., Conversa L., Murria L., Meseguer M. Time-lapse imaging: morphokinetic analysis of in vitro fertilization outcomes. Fertil. Steril. 2023; 120(2): 218-27. https://dx.doi.org/10.1016/j.fertnstert.2023.06.015
Riegler M.A., Stensen M.H., Witczak O., Andersen J.M., Hicks S.A., Hammer H.L. et al. Artificial intelligence in the fertility clinic: status, pitfalls and possibilities. Hum. Reprod. 2021; 36(9): 2429-42. https://dx.doi.org/10.1093/humrep/deab168
Luong T.M., Le N.Q.K. Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine. J. Assist. Reprod. Genet. 2024; 41(2): 239-52. https://dx.doi.org/10.1007/s10815-023-02973-y
Diakiw S.M., Hall J.M.M., VerMilyea M.D., Amin J., Aizpurua J., Giardini L. et al. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Hum. Reprod. 2022; 37(8): 1746-59. https://dx.doi.org/10.1093/humrep/deac131
Paya E., Pulgarín C., Bori L., Colomer A., Naranjo V., Meseguer M. Deep learning system for classification of ploidy status using time-lapse videos. F. S. Sci. 2023; 4(3): 211-8. https://dx.doi.org/10.1016/j.xfss.2023.06.002
Драпкина Ю.С., Макарова Н.П., Васильев Р.А., Амелин В.В., Франкевич В.Е., Калинина Е.А. Изучение аналитической обработки клинико-анамнестических и эмбриологических данных пациентов в программе вспомогательных репродуктивных технологий различными методами машинного обучения. Акушерство и гинекология. 2024; 3: 96-107. [Drapkina Yu.S., Makarova N.P., Vasilev R.A., Amelin V.V., Frankevich V.E., Kalinina E.A. Application of various machine learning techniques to the analysis of clinical, anamnestic, and embryological data of patients undergoing assisted reproductive technologies. Obstetrics and Gynecology. 2024; (3): 96-107 (in Russian)]. https://dx.doi.org/10.18565/aig.2023.281
Bori L., Paya E., Alegre L., Viloria T.A., Remohi J.A., Naranjo V. et al. 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; 114(6): 1232-41. https://dx.doi.org/10.1016/j.fertnstert.2020.08.023
Министерство здравоохранения Российской Федерации. Клинические рекомендации. Женское бесплодие. М.; 2024. [Ministry of Health of the Russian Federation. Clinical guidelines. Female infertility. Moscow; 2024. (in Russian)].
Российская Ассоциация Репродукции Человека. Секция «Клиническая эмбриология». Оценка ооцитов и эмбрионов в лаборатории ВРТ. Методические рекомендации. М.; 2021. 17 с. [Russian Association of Human Reproduction. Section "Clinical embryology". Assessment of oocytes and embryos in the ART laboratory. Methodological recommendations. Moscow; 2021. 17 p. (in Russian)].
ERICA. ERICA: artificial intelligence for embryo selection. Available at: https://embryoranking.com/
Chavez-Badiola A., Flores-Saiffe-Farías A., Mendizabal-Ruiz G., Drakeley A.J., Cohen J. Embryo ranking intelligent classification algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reprod. Biomed. Online. 2020; 41(4): 585-93. https://dx.doi.org/10.1016/j.rbmo.2020.07.003
Capalbo A., Poli M., Rienzi L., Girardi L., Patassini C., Fabiani M. et al. Mosaic human preimplantation embryos and their developmental potential in a prospective, non-selection clinical trial. Am. J. Hum. Genet. 2021; 108(12): 2238-47. https://dx.doi.org/10.1016/j.ajhg.2021.11.002
Salih M., Austin C., Warty R.R., Tiktin C., Rolnik D.L., Momeni M. et al. Embryo selection through artificial intelligence versus embryologists: a systematic review. Hum. Reprod. Open. 2023; 2023(3): hoad031. https://dx.doi.org/10.1093/hropen/hoad031
Xin X., Wu S., Xu H., Ma Y., Bao N., Gao M. et al. Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis. EClinicalMedicine. 2024; 77: 102897. https://dx.doi.org/10.1016/j.eclinm.2024.102897
Diakiw S.M., Hall J.M.M., VerMilyea M.D., Amin J., Aizpurua J., Giardini L. et al. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Hum. Reprod. 2022; 37(8): 1746-59. https://dx.doi.org/10.1093/humrep/deac131
Diakiw S.M., Hall J.M.M., VerMilyea M., Lim A.Y.X., Quangkananurug W., Chanchamroen S. et al. An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos. Reprod. Biomed. Online. 2022; 45(6): 1105-17. https://dx.doi.org/10.1016/j.rbmo.2022.07.018

Received 21.04.2025

Accepted 18.08.2025

Alfiya G. Yashchuk, Dr. Med. Sci., Professor, Head of the Department of Obstetrics and Gynaecology No. 2, Bashkir State Medical University, Ministry of Health of Russia, 450008, Russia, Republic of Bashkortostan, Ufa, Lenina str., 3, +7(917)343-17-15, https://orcid.org/0000-0003-2645-1662
Daria D. Gromenko, PhD student at the Department of Obstetrics and Gynaecology No. 2, Bashkir State Medical University, Ministry of Health of Russia, 450008, Russia, Republic of Bashkortostan, Ufa, Lenina str., 3, +7(987)473-86-19, https://orcid.org/0000-0001-5638-1779
Svetlana F. Nasyrova, PhD, Associate Professor, Department of Obstetrics and Gynaecology No. 2, Bashkir State Medical University, Ministry of Health of Russia, 450008, Russia, Republic of Bashkortostan, Ufa, Lenina str., 3, +7(919)615-46-52, https://orcid.org/0000-0002-2313-7232
Iuliia Iu. Gromenko, PhD, Chief Physician, Medical Centre "Family", 450054, Russia, Republic of Bashkortostan, Ufa, Oktyabrya Ave., 73 build. 1, +7(917)348-69-86,
https://orcid.org/0000-0002-3373-0873
Corresponding author: Daria D. Gromenko, dasha.gromenko@mail.ru

Evaluation of embryonic ploidy

Yashchuk A.G., Gromenko D.D., Nasyrova S.F., Gromenko Iu.Iu.

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