Fanton M, Cho JH, Baker VL, Loewke K. A higher number of oocytes retrieved is associated with an increase in fertilized oocytes, blastocysts, and cumulative live birth rates. Fertil Steril. 2023;119:762–9. https://doi.org/10.1016/j.fertnstert.2023.01.001.

Article  CAS  PubMed  Google Scholar 

Kim HH. More is better: oocyte number and cumulative live birth rate. Fertil Steril. 2023;119:770–1. https://doi.org/10.1016/j.fertnstert.2023.03.027.

Article  PubMed  Google Scholar 

Wang P, Zhao C, Xu W, Jin X, Zhang S, Zhu H. The association between the number of oocytes retrieved and cumulative live birth rate in different female age strata. Sci Rep. 2023;13:14516. https://doi.org/10.1038/s41598-023-41842-7.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Correia KFB, Missmer SA, Weinerman R, Ginsburg ES, Rossi BV. Development of a model to estimate the optimal number of oocytes to attempt to fertilize during assisted reproductive technology treatment. JAMA Netw Open. 2023;6:e2249395. https://doi.org/10.1001/jamanetworkopen.2022.49395.

Article  PubMed  PubMed Central  Google Scholar 

Ferrand T, Boulant J, He C, Chambost J, Jacques C, Pena C-A, et al. Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning. Hum Reprod. 2023;38:1918–26. https://doi.org/10.1093/humrep/dead163.

Article  PubMed  PubMed Central  Google Scholar 

La Marca A, Donno V, Longo M, Greco P, Cucinelli F, Varricchio MT, et al. Predicting the total number of retrieved oocytes following double ovarian stimulation (DuoStim). Hum Reprod. 2023;38:1784–8. https://doi.org/10.1093/humrep/dead148.

Article  CAS  PubMed  Google Scholar 

Han Y, Xu H, Feng G, Alpadi K, Chen L, Wang H, et al. An online tool using basal or activated ovarian reserve markers to predict the number of oocytes retrieved following controlled ovarian stimulation: a prospective observational cohort study. Front Endocrinol. 2022;13:881983. https://doi.org/10.3389/fendo.2022.881983.

Article  Google Scholar 

Liu Y, Pan Z, Wu Y, Song J, Chen J. Comparison of anti-Müllerian hormone and antral follicle count in the prediction of ovarian response: a systematic review and meta-analysis. J Ovarian Res. 2023;16:117. https://doi.org/10.1186/s13048-023-01202-5.

Article  PubMed  PubMed Central  Google Scholar 

Peigné M, Bernard V, Dijols L, Creux H, Robin G, Hocké C, et al. Using serum anti-Müllerian hormone levels to predict the chance of live birth after spontaneous or assisted conception: a systematic review and meta-analysis. Hum Reprod. 2023;38:1789–806. https://doi.org/10.1093/humrep/dead147.

Article  PubMed  Google Scholar 

Iwase A, Asada Y, Sugishita Y, Osuka S, Kitajima M, Kawamura K. Anti-Müllerian hormone for screening, diagnosis, evaluation, and prediction: a systematic review and expert opinions. J Obstet Gynaecol Res. 2024;50(1):15–39. https://doi.org/10.1111/jog.15818.

Article  CAS  PubMed  Google Scholar 

Sellami I, Barbotin AL, Bernard V, Robin G, Catteau-Jonard S, Sonigo C, et al. Anti-Mullerian hormone assessment in assisted reproductive technique outcome and natural conception. Semin Reprod Med. 2024;42:25–33. https://doi.org/10.1055/s-0044-1787273.

Article  PubMed  Google Scholar 

Bozkurt B, Erdem M, Mutlu MF, Erdem A, Guler I, Mutlu I, et al. Comparison of age-related changes in anti-Müllerian hormone levels and other ovarian reserve tests between healthy fertile and infertile population. Hum Fertil. 2016;19:192–8. https://doi.org/10.1080/14647273.2016.1217431.

Article  CAS  Google Scholar 

Tian CH, Liu LY, Huang YF, Yang HJ, Lai YY, Li CL, et al. Clinical prediction models for in vitro fertilization outcomes: a systematic review, meta-analysis, and external validation. Hum Reprod. 2025;40:633–46. https://doi.org/10.1093/humrep/deaf013.

Article  CAS  PubMed  Google Scholar 

Jamalirad H, Jajroudi M, Khajehpour B, Sadighi Gilani MA, Eslami S, Sabbaghian M, et al. AI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review. Hum Reprod Open. 2025;2025:hoae070. https://doi.org/10.1093/hropen/hoae070.

Article  PubMed  Google Scholar 

Kazijevs M, Samad MD. Deep imputation of missing values in time series health data: a review with benchmarking. J Biomed Inform. 2023;144:104440. https://doi.org/10.1016/j.jbi.2023.104440.

Article  PubMed  PubMed Central  Google Scholar 

Iwase A, Hasegawa Y, Tsukui Y, Kobayashi M, Hiraishi H, Nakazato T, et al. Anti-Müllerian hormone beyond an ovarian reserve marker: the relationship with the physiology and pathology in the life-long follicle development. Front Endocrinol. 2023;14:1273966. https://doi.org/10.3389/fendo.2023.1273966.

Article  Google Scholar 

Le LP, Nguyen T, Riegler MA, Halvorsen P, Nguyen BT. Multimodal missing data in healthcare: a comprehensive review and future directions. Comput Sci Rev. 2025;56:100720. https://doi.org/10.1016/j.cosrev.2024.100720.

Article  Google Scholar 

McLernon DJ, Raja E-A, Toner JP, Baker VL, Doody KJ, Seifer DB, et al. Predicting personalized cumulative live birth following in vitro fertilization. Fertil Steril. 2022;117:326–38. https://doi.org/10.1016/j.fertnstert.2021.09.015.

Article  PubMed  Google Scholar 

Liu H, Chen L, Shan G, Sun C, Lu C, Liao H, et al. An interpretable artificial intelligence approach to differentiate between blastocysts with similar or same morphological grades. Hum Reprod. 2025;40:1077–86. https://doi.org/10.1093/humrep/deaf066.

Article  PubMed  Google Scholar 

Yoo JH, Kim HO, Cha SW, Park CW, Yang KM, Song IO, et al. Age specific serum anti-Müllerian hormone levels in 1,298 Korean women with regular menstruation. Clin Exp Reprod Med. 2011;38:93–7. https://doi.org/10.5653/cerm.2011.38.2.93.

Article  PubMed  PubMed Central  Google Scholar 

Cui Y, Shi Y, Cui L, Han T, Gao X, Chen Z-J. Age-specific serum antimüllerian hormone levels in women with and without polycystic ovary syndrome. Fertil Steril. 2014;102:230-236.e2. https://doi.org/10.1016/j.fertnstert.2014.03.032.

Article  CAS  PubMed  Google Scholar 

Hao Y, Yang R, Li J, Zhou Z, Qian W, Zhang J, et al. Age-specific random day serum antimüllerian hormone reference values for women of reproductive age in the general population: a large Chinese nationwide population-based survey. Am J Obstet Gynecol. 2022;227:883.e1-883.e18. https://doi.org/10.1016/j.ajog.2022.07.029.

Article  CAS  PubMed  Google Scholar 

Arce J-C, Nyboe Andersen A, Fernández-Sánchez M, Visnova H, Bosch E, García-Velasco JA, et al. Ovarian response to recombinant human follicle-stimulating hormone: a randomized, antimüllerian hormone–stratified, dose–response trial in women undergoing in vitro fertilization/intracytoplasmic sperm injection. Fertil Steril. 2014;102:1633-1640.e5. https://doi.org/10.1016/j.fertnstert.2014.08.013.

Article  CAS  PubMed  Google Scholar 

Lobo R, Falahati A, Moley K, Pinborg A, Santos-Ribeiro S, Macklon NS, et al. Oocyte yield and live birth rate after follitropin delta dosing and fresh embryo transfer: an individual patient data meta-analysis. Reprod Biomed Online. 2025;50:104451. https://doi.org/10.1016/j.rbmo.2024.104451.

Article  CAS  PubMed  Google Scholar 

Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining. Association for Computing Machinery: New York; 2016. pp. 785–794. https://doi.org/10.1145/2939672.2939785.

Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y. LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st International conference on neural information processing systems. Curran Associates Inc.: Red Hook; 2017. pp. 3149–3157. https://doi.org/10.5555/3294996.3295074.

Lundberg S, Lee S-I. A unified approach to interpreting model predictions. In: Proceedings of the 31st International conference on neural information processing systems (NIPS'17). Curran Associates Inc.: Red Hook; 2017. pp. 4768–4777. https://doi.org/10.48550/arXiv.1705.07874. Accessed 20 Feb 2026.