Volume 9, Issue 4 (2025)
SJMR 2025, 9(4): 231-237 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Shafti V, Azarboo A. Personalized Medicine and Artificial Intelligence in Ovarian Stimulation Protocols for Female Infertility: A Review Article. SJMR 2025; 9 (4) : 7
URL: http://saremjrm.com/article-1-352-en.html
URL: http://saremjrm.com/article-1-352-en.html
1- School of Medicine, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran
2- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
2- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Abstract: (1112 Views)
Introduction: In vitro fertilization (IVF) assisted by artificial intelligence (AI) is a rapidly evolving research field. This approach enhances ovarian stimulation outcomes and efficiency, personalizes drug dosage and timing, and streamlines the IVF process, ultimately leading to improved clinical outcomes.
Materials and Methods: This study was conducted as a systematic review. Relevant literature was retrieved from reputable scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar. Articles published between 2010 and 2024 on personalized medicine, AI, and ovarian stimulation protocols in female infertility were reviewed. The selected keywords included "artificial intelligence," "personalized medicine," "ovarian stimulation," "female infertility," "IVF treatment," "clinical decision-making," and "decision support systems." Studies were screened based on predefined inclusion and exclusion criteria, encompassing original research articles, clinical trials, systematic reviews, and meta-analyses. Data from the selected studies were extracted and analyzed to identify current trends, potential applications, advantages, challenges, and future directions for integrating AI into ovarian stimulation protocols. Furthermore, the role of advanced technologies such as machine learning (ML), artificial neural networks (ANN), and clinical decision support systems (CDSS) in optimizing the IVF process was examined.
Conclusion: This review aimed to provide an overview of the role of personalized medicine and AI in ovarian stimulation protocols for infertility treatment. This innovative approach not only plays a crucial role in IVF clinics but also enhances treatment outcomes and reduces pregnancy-related complications.
Materials and Methods: This study was conducted as a systematic review. Relevant literature was retrieved from reputable scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar. Articles published between 2010 and 2024 on personalized medicine, AI, and ovarian stimulation protocols in female infertility were reviewed. The selected keywords included "artificial intelligence," "personalized medicine," "ovarian stimulation," "female infertility," "IVF treatment," "clinical decision-making," and "decision support systems." Studies were screened based on predefined inclusion and exclusion criteria, encompassing original research articles, clinical trials, systematic reviews, and meta-analyses. Data from the selected studies were extracted and analyzed to identify current trends, potential applications, advantages, challenges, and future directions for integrating AI into ovarian stimulation protocols. Furthermore, the role of advanced technologies such as machine learning (ML), artificial neural networks (ANN), and clinical decision support systems (CDSS) in optimizing the IVF process was examined.
Conclusion: This review aimed to provide an overview of the role of personalized medicine and AI in ovarian stimulation protocols for infertility treatment. This innovative approach not only plays a crucial role in IVF clinics but also enhances treatment outcomes and reduces pregnancy-related complications.
Article number: 7
Keywords: Artificial Intelligence, Personalized Medicine, Decision Support Systems, Assisted Reproductive Technology (ART), Infertility
Article Type: Systematical Review |
Subject:
Sterility
Received: 2025/01/14 | Accepted: 2025/02/8 | Published: 2025/03/16
Received: 2025/01/14 | Accepted: 2025/02/8 | Published: 2025/03/16
References
1. Rezaeiye, R.D., et al., Impact of various parameters as predictors of the success rate of in vitro fertilization. International Journal of Fertility & Sterility, 2022. 16(2): p. 76.
2. Smeenk, J., et al., ART in Europe, 2019: results generated from European registries by ESHRE. Human Reproduction, 2023. 38(12): p. 2321-2338. [DOI:10.1093/humrep/dead197] [PMID] []
3. Datta, A.K., et al., Mild versus conventional ovarian stimulation for IVF in poor, normal and hyper-responders: a systematic review and meta-analysis. Human reproduction update, 2021. 27(2): p. 229-253. [DOI:10.1093/humupd/dmaa035] [PMID] []
4. Abbara, A., et al., FSH requirements for follicle growth during controlled ovarian stimulation. Frontiers in endocrinology, 2019. 10: p. 579. [DOI:10.3389/fendo.2019.00579] [PMID] []
5. Shrestha, D., X. La, and H.L. Feng, Comparison of different stimulation protocols used in in vitro fertilization: a review. Annals of translational medicine, 2015. 3(10): p. 137.
6. Hanassab, S., et al., The prospect of artificial intelligence to personalize assisted reproductive technology. Npj digital medicine, 2024. 7(1): p. 55. [DOI:10.1038/s41746-024-01006-x] [PMID] []
7. Medenica, S., et al., The future is coming: artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes-the value of regulatory frameworks. Diagnostics, 2022. 12(12): p. 2979. [DOI:10.3390/diagnostics12122979] [PMID] []
8. Mañas, N.C., et al., P-637 Development and validation of an Artificial Intelligence algorithm that matches a clinician ability to select the best follitropin dose for ovarian stimulation. Human Reproduction, 2021. 36(Supplement_1): p. deab130. 636. [DOI:10.1093/humrep/deab130.636]
9. Choo, C.-W. and J.H. Kim, A prospective cohort study to develop a treatment algorithm for controlled ovarian stimulation to select a starting dose of recombinant follicle stimulating hormone based on patient characteristics and ovarian response (fame study). Fertility and Sterility, 2021. 116(3): p. e196. [DOI:10.1016/j.fertnstert.2021.07.538]
10. Ferrand, T., et al., Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning. Human Reproduction, 2023. 38(10): p. 1918-1926. [DOI:10.1093/humrep/dead163] [PMID] []
11. Hajirasouliha, I. and O. Elemento, Precision medicine and artificial intelligence: overview and relevance to reproductive medicine. Fertility and Sterility, 2020. 114(5): p. 908-913. [DOI:10.1016/j.fertnstert.2020.09.156] [PMID]
12. Niederberger, C., et al., Forty years of IVF. Fertility and sterility, 2018. 110(2): p. 185-324. e5. [DOI:10.1016/j.fertnstert.2018.05.017] [PMID]
13. Davenport, T. and R. Kalakota, The potential for artificial intelligence in healthcare. Future healthcare journal, 2019. 6(2): p. 94-98. [DOI:10.7861/futurehosp.6-2-94] [PMID] []
14. Albertini, D.F., The making and managing of a niche for artificial intelligence in reproductive medicine. Journal of Assisted Reproduction and Genetics, 2023. 40(2): p. 211-212. [DOI:10.1007/s10815-023-02744-9] [PMID] []
15. Hariton, E., et al., Applications of artificial intelligence in ovarian stimulation: a tool for improving efficiency and outcomes. Fertility and sterility, 2023. 120(1): p. 8-16. [DOI:10.1016/j.fertnstert.2023.05.148] [PMID]
16. Letterie, G. and A. Mac Donald, Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertility and Sterility, 2020. 114(5): p. 1026-1031. [DOI:10.1016/j.fertnstert.2020.06.006] [PMID]
17. Letterie, G., Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies. Journal of Assisted Reproduction and Genetics, 2021. 38(7): p. 1617-1625. [DOI:10.1007/s10815-021-02159-4] [PMID] []
18. Xia, L., et al., Predicting personalized cumulative live birth rate after a complete in vitro fertilization cycle: an analysis of 32,306 treatment cycles in China. Reproductive Biology and Endocrinology, 2024. 22(1): p. 65. [DOI:10.1186/s12958-024-01237-3] [PMID] []
19. Wald, K., et al., Changing stimulation protocol on repeat conventional ovarian stimulation cycles does not lead to improved laboratory outcomes. Fertility and Sterility, 2021. 116(3): p. 757-765. [DOI:10.1016/j.fertnstert.2021.04.030] [PMID]
20. Li, Y., et al., A novel nomogram for individualized gonadotropin starting dose in GnRH antagonist protocol. Frontiers in Endocrinology, 2021. 12: p. 688654. [DOI:10.3389/fendo.2021.688654] [PMID] []
21. Murillo, F., et al., Causal inference indicates that poor responders have similar outcomes with the antagonist protocol compared with flare. Fertility and sterility, 2023. 120(2): p. 289-296. [DOI:10.1016/j.fertnstert.2023.04.007] [PMID]
22. Broekmans, F.J., Individualization of FSH doses in assisted reproduction: facts and fiction. Frontiers in endocrinology, 2019. 10: p. 181. [DOI:10.3389/fendo.2019.00181] [PMID] []
23. Xu, H., et al., POvaStim: An online tool for directing individualized FSH doses in ovarian stimulation. The Innovation, 2023. 4(2). [DOI:10.1016/j.xinn.2023.100401] [PMID] []
24. Letterie, G., A. MacDonald, and Z. Shi, An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions. Reproductive biomedicine online, 2022. 44(2): p. 254-260. [DOI:10.1016/j.rbmo.2021.10.006] [PMID]
25. Cheng, S., et al., Comparative outcomes of AI-assisted ChatGPT and face-to-face consultations in infertility patients: a cross-sectional study. Postgraduate Medical Journal, 2024. 100(1189): p. 851-855. [DOI:10.1093/postmj/qgae083] [PMID]
26. Liang, X., et al., Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound. Reproductive biomedicine online, 2022. 45(6): p. 1197-1206. [DOI:10.1016/j.rbmo.2022.07.012] [PMID]
27. Chung, E.H., et al., Virtual compared with in-clinic transvaginal ultrasonography for ovarian reserve assessment. Obstetrics & Gynecology, 2022. 139(4): p. 561-570. [DOI:10.1097/AOG.0000000000004698] [PMID] []
28. Abbara, A., S.A. Clarke, and W.S. Dhillo, Novel concepts for inducing final oocyte maturation in in vitro fertilization treatment. Endocrine reviews, 2018. 39(5): p. 593-628. [DOI:10.1210/er.2017-00236] [PMID] []
29. Hariton, E., et al., A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes. Fertility and Sterility, 2021. 116(5): p. 1227-1235. [DOI:10.1016/j.fertnstert.2021.06.018] [PMID]
30. Fanton, M., et al., An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation. Fertility and Sterility, 2022. 118(1): p. 101-108. [DOI:10.1016/j.fertnstert.2022.04.003] [PMID]
31. Dhillon, R., et al., Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool. Human Reproduction, 2016. 31(1): p. 84-92. [DOI:10.1093/humrep/dev268] [PMID]
32. Cai, Q., et al., Quality of embryos transferred and progesterone levels are the most important predictors of live birth after fresh embryo transfer: a retrospective cohort study. Journal of Assisted Reproduction and Genetics, 2014. 31: p. 185-194. [DOI:10.1007/s10815-013-0129-4] [PMID] []
33. Bulletti, C., et al., Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications. Mayo Clinic Proceedings: Digital Health, 2024. [DOI:10.1016/j.mcpdig.2024.08.007] [PMID] []
34. Mendizabal-Ruiz, G., et al., Artificial intelligence in human reproduction. Archives of Medical Research, 2024. 55(8): p. 103131. [DOI:10.1016/j.arcmed.2024.103131] [PMID]
35. Sellami, A., et al., # 364: Precision Medicine and Artificial Intelligence in the Area of Infertility: Update Applications and Perspectives. Fertility & Reproduction, 2023. 5(04): p. 327-327. [DOI:10.1142/S2661318223741383]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
