Evaluation of embryonic ploidy

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Resumo

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.

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Sobre autores

Alfiya Yashchuk

Bashkir State Medical University, Ministry of Health of the Russian Federation

Email: dasha.gromenko@mail.ru

Dr. Med. Sci., Professor, Head of the Department of Obstetrics and Gynaecology No. 2

Rússia, 450008, Republic of Bashkortostan, Ufa, Lenina str., 3

Daria Gromenko

Bashkir State Medical University, Ministry of Health of the Russian Federation

Autor responsável pela correspondência
Email: dasha.gromenko@mail.ru
ORCID ID: 0000-0001-5638-1779

PhD student at the Department of Obstetrics and Gynaecology No. 2

Rússia, 450008, Republic of Bashkortostan, Ufa, Lenina str., 3

Svetlana Nasyrova

Bashkir State Medical University, Ministry of Health of the Russian Federation

Email: dasha.gromenko@mail.ru
ORCID ID: 0000-0002-2313-7232

PhD, Associate Professor, Department of Obstetrics and Gynaecology No. 2

Rússia, 450008, Republic of Bashkortostan, Ufa, Lenina str., 3

Iuliia Gromenko

Medical Center "Family"

Email: dasha.gromenko@mail.ru
ORCID ID: 0000-0002-3373-0873

PhD, Chief Physician

Rússia, 450054, Republic of Bashkortostan, Ufa, Oktyabrya Ave., 73 build. 1

Bibliografia

  1. 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
  2. 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
  3. 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)].
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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)].
  17. 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)].
  18. ERICA. ERICA: artificial intelligence for embryo selection. Available at: https://embryoranking.com/
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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

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