EMBRYO TRANSFER IN YOUNG PATIENTS USING AI
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Abstract
Despite the fact that a woman's young age is a key factor in the effectiveness of ART programs, the pregnancy rate per treatment cycle remains low in patients under the age of 35, highlighting the need to improve the outcomes of IVF programs. The use of AI technologies to select the optimal embryo for transfer may enhance the results, but more data is needed to confirm this hypothesis.
The aim of this study is to investigate whether the selection of embryos using AI improves the outcomes of IVF/ICSI programs in women under the age of 35.
Material and methods. A prospective randomized study included 120 patients aged 18-34 who underwent IVF/ICSI at the Semya Medical Center (Ufa) from January 1, 2024, to April 1, 2024. The results of IVF/ICSI protocols were analyzed in patients divided into groups depending on the embryo selection method: group 1 (experimental, n = 60) – selection using AI ERICA 1.0; group 2 (control, n = 60) – routine morphological assessment using the Gardner scale.
Results. In the group where embryo selection was performed using AI (n = 60), 31 clinical pregnancies (51.7%) were registered, compared to 24 pregnancies (40.0%) in the control group (p = 0.200). In the experimental group, 2 cases ended in spontaneous abortion (6.5% of all pregnancies), while in the control group, 3 cases ended in spontaneous abortion (12.5%) (p = 0.440).
Conclusion. The results of the study show that AI can be a promising tool for selecting embryos for transfer. However, additional studies with a larger sample size are needed for final conclusions.
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References
1. The ESHRE Guideline Group on Ovarian Stimulation; Ata B, Bosch E, Broer S, Griesinger G, Grynberg M, Kolibianakis E, et al. Ovarian stimulation for IVF/ICSI: evidence-based guideline. Hum Reprod Open. 2020; 2020(2): hoaa009. doi: 10.1093/hropen/hoaa009
2. Law YJ, Zhang N, Venetis CA, Chambers GM, Harris K. The number of oocytes associated with maximum cumulative live birth rates per aspiration depends on female age: a population study of 221 221 treatment cycles. Hum Reprod. 2019; 34(9): 1778-1787. doi: 10.1093/humrep/dez100
3. Korsak VS, Smirnova AA, Shurygina OV. ART Registry of the All-Russian Public Organization «Russian Association of Human Reproduction». Report for 2023. Russian Journal of Human Reproduction. 2025; 31(6): 6-22. Russian (Корсак В.С., Смирнова А.А., Шурыгина О.В. Регистр ВРТ Общероссийской общественной организации «Российская Ассоциация Репродукции Человека». Отчет за 2023 год //Проблемы репродукции. 2025. Т. 31, № 6. С. 6-22.) doi:10.17116/repro2025310616
4. ESHRE Guideline Group on the Number of Embryos to Transfer; Alteri A, Arroyo G, Baccino G, Craciunas L, De Geyter C, Ebner T, et al. ESHRE guideline: number of embryos to transfer during IVF/ICSI†. Hum Reprod. 2024; 39(4): 647-657. doi: 10.1093/humrep/deae010
5. 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. doi: 10.1038/s41746-019-0096-y
6. Kucherov A, Fazzari M, Lieman H, Ball GD, Doody K, Jindal S. PGT-A is associated with reduced cumulative live birth rate in first reported IVF stimulation cycles age ≤ 40: an analysis of 133,494 autologous cycles reported to SART CORS. J Assist Reprod Genet. 2023; 40(1): 137-149. doi: 10.1007/s10815-022-02667-x
7. Salih M, Austin C, Warty RR, Tiktin C, Rolnik DL, Momeni M, et al. Embryo selection through artificial intelligence versus embryologists: a systematic review. Hum Reprod Open. 2023; 2023(3): hoad031. doi: 10.1093/hropen/hoad031
8. 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. E Clinical Medicine. 2024; 77: 102897. doi: 10.1016/j.eclinm.2024.102897
9. Hall JMM, Nguyen TV, Dinsmore AW, Perugini D, Perugini M, Fukunaga N, et al. Use of federated learning to develop an artificial intelligence model predicting usable blastocyst formation from pre-ICSI oocyte images. Reprod Biomed Online. 2024; 49(6): 104403. doi: 10.1016/j.rbmo.2024.104403
10. Bormann CL, Thirumalaraju P, Kanakasabapathy MK, Kandula H, Souter I, Dimitriadis I, et al. Consistency and objectivity of automated embryo assessments using deep neural networks. Fertil Steril. 2020; 113(4): 781-787.e1. doi: 10.1016/j.fertnstert.2019.12.004
11. Diakiw SM, Hall JMM, VerMilyea MD, 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): 1746-1759. doi: 10.1093/humrep/deac131
12. Dimitriadis I, Zaninovic N, Badiola AC, Bormann CL. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online. 2022; 44(3): 435-448. doi: 10.1016/j.rbmo.2021.11.003
13. Shingshetty L, Cameron NJ, McLernon DJ, Bhattacharya S. Predictors of success after in vitro fertilization. Fertil Steril. 2024; 121(5): 742-751. doi: 10.1016/j.fertnstert.2024.03.003
14. Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril. 2020; 114(5): 914-920. doi: 10.1016/j.fertnstert.2020.09.157
15. Yashchuk AG, Gromenko DD, Nasyrova SF, Gromenko YY. Possibilities of assessing embryo ploidy. Obstetrics and Gynecology. 2025; (9): 126-133. Russian (Ящук А.Г., Громенко Д.Д., Насырова С.Ф., Громенко Ю.Ю. Возможности оценки плоидности эмбрионов //Акушерство и гинекология. 2025. № 9. С. 126-133.) doi: 10.18565/aig.2025.106
16. 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(4): 770-784. doi: 10.1093/humrep/deaa013
17. Fitz VW, Kanakasabapathy MK, Thirumalaraju P, Kandula H, Ramirez LB, Boehnlein L, et al. Should there be an «AI» in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm. J Assist Reprod Genet. 2021; 38(10): 2663-2670. doi: 10.1007/s10815-021-02318-7