Abstract
Our aim was to develop an AI-based NC-FET Treatment Management Algorithm (NTMA), decision-support system that predicts ovulation in real time to manage and optimize natural frozen embryo transfer cycle (NC-FET) scheduling. The algorithm was developed using a “teacher-student” machine learning approach and was trained on a total of 3,975 labeled NC-FET, including 3,432 training cycles and 543 test cycles. A second test group included 166 documented ovulation cycles (documented follicular rupture and LH surge in two consecutive days of ultrasound scans). The algorithm showed high ovulation detection accuracy in both tests’ groups particularly one day before and the day of ovulation (95.4% and 94.6% in the labeled test group and 95.5% and 95.3% in the documented test group, respectively). Most influential predictive features of the algorithm included LH levels, the estrogen/progesterone ratio, and leading follicle size during the monitored test days. The NTMA yielded 92.04% correct prediction 7 in identifying ovulation with an average of 3.1 tests per cycle. We propose an AI-based algorithm for the complete management of a NC-FET. The algorithm shows high accuracy in predicting the time of ovulation and therefore can serve as a useful decision support tool for clinicians in their daily practice. Prospective studies are warranted to validate these results.
Data availability
Data is provided within the manuscript or supplementary information files.The data underlying this article will be shared on reasonable request to the corresponding author.
Code availability
The code used to develop and evaluate the NTMA is publicly available at https://github.com/fertilai/nc-fet-simulator/.
References
Venetis, C. A. Pro: Fresh versus frozen embryo transfer. Is frozen embryo transfer the future? Hum. Reprod. 37 (7), 1379–1387 (2022).
Roelens, C. & Blockeel, C. Impact of different endometrial preparation protocols before frozen embryo transfer on pregnancy outcomes: a review. Fertil. Steril. 118 (5), 820–827 (2022).
Mackens, S. et al. Frozen embryo transfer: a review on the optimal endometrial preparation and timing. Hum. Reprod. 32 (11), 2234–2242 (2017).
Roelens, C. et al. Artificially prepared vitrified-warmed embryo transfer cycles are associated with an increased risk of pre-eclampsia. Reprod. Biomed. Online. 44 (5), 915–922 (2022).
Vinsonneau, L. et al. Impact of endometrial preparation on early pregnancy loss and live birth rate after frozen embryo transfer: a large multicenter cohort study (14 421 frozen cycles). Hum. Reprod. Open. 2022(2), phoac007 (2022).
von Versen-Hoynck, F. & Griesinger, G. Should any use of artificial cycle regimen for frozen-thawed embryo transfer in women capable of ovulation be abandoned: yes, but what’s next for FET cycle practice and research? Hum. Reprod. 37 (8), 1697–1703 (2022).
Maheshwari, A. et al. Is frozen embryo transfer better for mothers and babies? Can cumulative meta-analysis provide a definitive answer? Hum. Reprod. Update. 24 (1), 35–58 (2018).
Moreno-Sepulveda, J., Espinos, J. J. & Checa, M. A. Lower risk of adverse perinatal outcomes in natural versus artificial frozen-thawed embryo transfer cycles: a systematic review and meta-analysis. Reprod. Biomed. Online. 42 (6), 1131–1145 (2021).
Wu, H. et al. Endometrial preparation for frozen-thawed embryo transfer cycles: a systematic review and network meta-analysis. J. Assist. Reprod. Genet. 38 (8), 1913–1926 (2021).
Mumusoglu, S. et al. Preparation of the Endometrium for Frozen Embryo Transfer: A Systematic Review. Front. Endocrinol. (Lausanne). 12, 688237 (2021).
Erden, M. et al. The LH surge and ovulation re-visited: a systematic review and meta-analysis and implications for true natural cycle frozen thawed embryo transfer. Hum. Reprod. Update. 28 (5), 717–732 (2022).
Maman, E. et al. Prediction of ovulation: new insight into an old challenge. Sci. Rep. 13 (1), 20003 (2023).
Letterie, G. Artificial intelligence and assisted reproductive technologies: 2023. Ready for prime time? Or not. Fertil. Steril. 120 (1), 32–37 (2023).
Goyal, A., Kuchana, M. & Ayyagari, K. P. R. Machine learning predicts live-birth occurrence before in-vitro fertilization treatment. Sci. Rep. 10 (1), 20925 (2020).
Letterie, G., Mac, A. & Donald Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertil. Steril. 114 (5), 1026–1031 (2020).
Letterie, G., MacDonald, A. & Shi, Z. An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions. Reprod. Biomed. Online. 44 (2), 254–260 (2022).
McCallum, C. et al. Deep learning-based selection of human sperm with high DNA integrity. Commun. Biol. 2, 250 (2019).
Reuvenny, S. et al. An artificial intelligence-based approach for selecting the optimal day for triggering in antagonist protocol cycles. Reprod. Biomed. Online. 48 (1), 103423 (2024).
Hariton, E. et al. A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes. Fertil. Steril. 116 (5), 1227–1235 (2021).
Fanton, M. et al. An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation. Fertil. Steril. 118 (1), 101–108 (2022).
Youngster, M. et al. Artificial intelligence in the service of intrauterine insemination and timed intercourse in spontaneous cycles. Fertil. Steril. 120 (5), 1004–1012 (2023).
Youngster, M. et al. Intrauterine insemination timing models-LH can only take you so far. J. Assist. Reprod. Genet. 41 (7), 1843–1850 (2024).
Youngster, M. et al. Optimizing workload balance using artificial intelligence. Fertil. Steril. 122 (1), 178–180 (2024).
Zaninovic, N. & Rosenwaks, Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil. Steril. 114 (5), 914–920 (2020).
Diakiw, S. M. et al. An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos. Reprod. Biomed. Online. 45 (6), 1105–1117 (2022).
Luz, A. et al. Improved clinical pregnancy rates in natural frozen-thawed embryo transfer cycles with machine learning ovulation prediction: insights from a retrospective cohort study. Sci. Rep. 14 (1), 29451 (2024).
Hinton, G. Distilling the Knowledge in a Neural Network. arXiv 2015. preprint arXiv:1503.02531.
Wetzels, L. C. & Hoogland, H. J. Relation between ultrasonographic evidence of ovulation and hormonal parameters: luteinizing hormone surge and initial progesterone rise. Fertil. Steril. 37 (3), 336–341 (1982).
Marinho, A. O. et al. Real time pelvic ultrasonography during the periovulatory period of patients attending an artificial insemination clinic. Fertil. Steril. 37 (5), 633–638 (1982).
Irani, M. et al. Optimal parameters for determining the LH surge in natural cycle frozen-thawed embryo transfers. J. Ovarian Res. 10 (1), 70 (2017).
Groenewoud, E. R. et al. What is the optimal means of preparing the endometrium in frozen-thawed embryo transfer cycles? A systematic review and meta-analysis. Hum. Reprod. Update. 23 (2), 255–261 (2017).
Weissman, A. et al. Spontaneous ovulation versus HCG triggering for timing natural-cycle frozen-thawed embryo transfer: a randomized study. Reprod. Biomed. Online. 23 (4), 484–489 (2011).
Fatemi, H. M. et al. Cryopreserved-thawed human embryo transfer: spontaneous natural cycle is superior to human chorionic gonadotropin-induced natural cycle. Fertil. Steril. 94 (6), 2054–2058 (2010).
Montagut, M. et al. Frozen-thawed embryo transfers in natural cycles with spontaneous or induced ovulation: the search for the best protocol continues. Hum. Reprod. 31 (12), 2803–2810 (2016).
Gao, D. D. et al. Is human chorionic gonadotropin trigger beneficial for natural cycle frozen-thawed embryo transfer? Front. Med. (Lausanne). 8, 691428 (2021).
Acknowledgements
Data is provided within the manuscript or supplementary information files. The data underlying this article will be shared on reasonable request to the corresponding author.
Funding
This study was supported by FertilAI LTD.
Author information
Authors and Affiliations
Contributions
Eden Moran - Conception and design, acquisition of data, analysis and interpretation of data, approval of the version to be published, agree to be accountable for all aspects of the workAriel Hourvitz - Conception and design, analysis and interpretation of data, revising the article, approval of the version to be published, agree to be accountable for all aspects of the workAlmog Luz -Conception and design, acquisition of data, analysis and interpretation of data, drafting the article and revising, approval of the version to be published, agree to be accountable for all aspects of the work.Nevo Itzhak - Conception and design, acquisition of data, analysis and interpretation of data, drafting the article and revising, approval of the version to be published, agree to be accountable for all aspects of the workRohi Hourvitz - Conception and design, analysis and interpretation of data, approval of the version to be published, agree to be accountable for all aspects of the workMichal Youngster - Conception and design, analysis and interpretation of data, revising the article, approval of the version to be published, agree to be accountable for all aspects of the workMicha Baum - Conception and design, analysis and interpretation of data, revising the article, approval of the version to be published, agree to be accountable for all aspects of the workEttie Maman - Conception and design, analysis and interpretation of data, drafting the article and revising, approval of the version to be published, agree to be accountable for all aspects of the work.
Corresponding author
Ethics declarations
Competing interests
A.H, R.H, A.L, M.B and E.M are shareholders and board members of FertilAI. N.I and E.M are employees of FertilAI. M.Y reports no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Moran, E., Hourvitz, A., Luz, A. et al. Treatment management algorithm for natural frozen embryo transfer cycles using a real-time ovulation prediction machine learning model. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42921-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-42921-1
