{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:57:52Z","timestamp":1781632672305,"version":"3.54.5"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:00:00Z","timestamp":1769817600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:00:00Z","timestamp":1769990400000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003252","name":"Lund University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003252","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Predicting live birth after the first IVF\/ICSI treatment is challenging, as many factors may interact to affect IVF\/ICSI outcomes. Adenomyosis is one factor that impacts live birth rates. Machine learning algorithms have been shown valuable for detecting complex dependencies and predicting outcomes in different clinical settings. We aimed to develop a prediction model for live birth after IVF\/ICSI treatment, using the Extreme Gradient Boosting (XGBoost) algoritm and incorporating the revised Morphological Uterus Sonographic Assessment (MUSA) group features of adenomyosis. We used a machine learning model based on data from 1037 women undergoing their first IVF\/ICSI treatment between January 2019 and October 2022. The importance of each variable on the model was illustrated with the Shapley additive explanations algorithm (SHAP) variable importance. The prediction model was presented with the area under receiver operating characteristics curve (ROC). The proposed XGBoost model had a test AUC of 0.66 and accuracy of 0.59. S-AMH was the best variable for predicting live birth with a mean SHAP of 0.21, followed by a regular junctional zone as the best ultrasonographic variable, mean SHAP 0.13. The predictive ability of MUSA features in relation to live birth was limited. Additional variables should be included in future prediction models.<\/jats:p>","DOI":"10.1038\/s41598-025-31013-1","type":"journal-article","created":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T09:04:59Z","timestamp":1769850299000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning prediction of live birth after IVF using the morphological uterus sonographic assessment group features of adenomyosis"],"prefix":"10.1038","volume":"16","author":[{"given":"Sara","family":"Alson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ola","family":"Bj\u00f6rnsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emir","family":"Henic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan R.","family":"Hansson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Povilas","family":"Sladkevicius","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"key":"31013_CR1","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1097\/GCO.0b013e328338493f","volume":"22","author":"VA Moragianni","year":"2010","unstructured":"Moragianni, V. A. & Penzias, A. S. Cumulative live-birth rates after assisted reproductive technology. Curr. Opin. Obstet. Gynecol. 22, 189\u2013192 (2010).","journal-title":"Curr. Opin. Obstet. Gynecol."},{"key":"31013_CR2","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.fertnstert.2024.03.003","volume":"121","author":"L Shingshetty","year":"2024","unstructured":"Shingshetty, L., Cameron, N. J., McLernon, D. J. & Bhattacharya, S. Predictors of success after in vitro fertilization. Fertil. Steril. 121, 742\u2013751 (2024).","journal-title":"Fertil. Steril."},{"key":"31013_CR3","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/0002-9378(72)90781-8","volume":"112","author":"CC Bird","year":"1972","unstructured":"Bird, C. C., McElin, T. W. & Manalo-Estrella, P. The elusive adenomyosis of the uterus\u2013revisited. Am. J. Obstet. Gynecol. 112, 583\u2013593 (1972).","journal-title":"Am. J. Obstet. Gynecol."},{"key":"31013_CR4","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1093\/humrep\/deu041","volume":"29","author":"P Vercellini","year":"2014","unstructured":"Vercellini, P. et al. Uterine adenomyosis and in vitro fertilization outcome: a systematic review and meta-analysis. Hum. Reprod. 29, 964\u2013977 (2014).","journal-title":"Hum. Reprod."},{"key":"31013_CR5","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1002\/uog.14806","volume":"46","author":"T Van den Bosch","year":"2015","unstructured":"Van den Bosch, T. et al. Terms, definitions and measurements to describe sonographic features of myometrium and uterine masses: a consensus opinion from the morphological uterus sonographic assessment (MUSA) group. Ultrasound Obstet. Gynecol. 46, 284\u2013298 (2015).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"31013_CR6","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1002\/uog.24786","volume":"60","author":"MJ Harmsen","year":"2022","unstructured":"Harmsen, M. J. et al. Consensus on revised definitions of morphological uterus sonographic assessment (MUSA) features of adenomyosis: results of modified Delphi procedure. Ultrasound Obstet. Gynecol. 60, 118\u2013131 (2022).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"31013_CR7","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1002\/uog.15955","volume":"48","author":"S Guerriero","year":"2016","unstructured":"Guerriero, S. et al. Systematic approach to sonographic evaluation of the pelvis in women with suspected endometriosis, including terms, definitions and measurements: a consensus opinion from the international deep endometriosis analysis (IDEA) group. Ultrasound Obstet. Gynecol. 48, 318\u2013332 (2016).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"31013_CR8","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/j.fertnstert.2022.07.024","volume":"118","author":"S Alson","year":"2022","unstructured":"Alson, S., Jokubkiene, L., Henic, E. & Sladkevicius, P. Prevalence of endometrioma and deep infiltrating endometriosis at transvaginal ultrasound examination of subfertile women undergoing assisted reproductive treatment. Fertil. Steril. 118, 915\u2013923 (2022).","journal-title":"Fertil. Steril."},{"key":"31013_CR9","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1111\/aogs.14812","volume":"103","author":"S Alson","year":"2024","unstructured":"Alson, S., Jokubkiene, L., Henic, E. & Sladkevicius, P. Prevalence of adenomyosis features in women scheduled for assisted reproductive treatment, using the morphological uterus sonographic assessment group definitions. Acta Obstet. Gynecol. Scand. 103, 1142\u20131152 (2024).","journal-title":"Acta Obstet. Gynecol. Scand."},{"key":"31013_CR10","doi-asserted-by":"publisher","first-page":"20925","DOI":"10.1038\/s41598-020-76928-z","volume":"10","author":"A Goyal","year":"2020","unstructured":"Goyal, A., Kuchana, M. & Ayyagari, K. P. R. Machine learning predicts live-birth occurrence before in-vitro fertilization treatment. Sci. Rep. 10, 20925 (2020).","journal-title":"Sci. Rep."},{"key":"31013_CR11","doi-asserted-by":"publisher","first-page":"22631","DOI":"10.1038\/s41598-025-06998-4","volume":"15","author":"WJ Huo","year":"2025","unstructured":"Huo, W. J., Peng, F., Quan, S. & Wang, X. C. Development and validation of machine learning models for predicting blastocyst yield in IVF cycles. Sci. Rep. 15, 22631 (2025).","journal-title":"Sci. Rep."},{"key":"31013_CR12","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/s10916-022-01890-4","volume":"46","author":"S Van Laere","year":"2022","unstructured":"Van Laere, S., Muylle, K. M., Dupont, A. G. & Cornu, P. Machine learning techniques outperform conventional statistical methods in the prediction of high risk QTc prolongation related to a Drug-Drug interaction. J. Med. Syst. 46, 100 (2022).","journal-title":"J. Med. Syst."},{"key":"31013_CR13","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1007\/s10815-020-01908-1","volume":"37","author":"Z Barnett-Itzhaki","year":"2020","unstructured":"Barnett-Itzhaki, Z. et al. Machine learning vs. classic statistics for the prediction of IVF outcomes. J. Assist. Reprod. Genet. 37, 2405\u20132412 (2020).","journal-title":"J. Assist. Reprod. Genet."},{"key":"31013_CR14","doi-asserted-by":"publisher","first-page":"32083","DOI":"10.1038\/s41598-024-83781-x","volume":"14","author":"J Peng","year":"2024","unstructured":"Peng, J. et al. Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes. Sci. Rep. 14, 32083 (2024).","journal-title":"Sci. Rep."},{"key":"31013_CR15","doi-asserted-by":"crossref","unstructured":"Chen, T. G.C. XGBoost: a scalable tree boosting system. The International Conference on Knowledge Discovery and Data Mining, 785\u2013794 (2016).","DOI":"10.1145\/2939672.2939785"},{"key":"31013_CR16","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1002\/uog.12469","volume":"41","author":"S Reid","year":"2013","unstructured":"Reid, S. & Condous, G. Transvaginal sonographic sliding sign: accurate prediction of pouch of Douglas obliteration. Ultrasound Obstet. Gynecol. 41, 605\u2013607 (2013).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"31013_CR17","doi-asserted-by":"publisher","first-page":"2540","DOI":"10.1111\/aogs.14986","volume":"12","author":"S Alson","year":"2024","unstructured":"Alson, S., Henic, E., Hansson, S. R. & Sladkevicius, P. Correlation of adenomyosis features to live birth rates after the first IVF\/ICSI treatment, when using the revised morphological uterus sonographic assessment group definitions. Acta Obstet. Gynecol. Scand. 12, 2540\u20132553 (2024).","journal-title":"Acta Obstet. Gynecol. Scand."},{"key":"31013_CR18","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1097\/00001703-199906000-00013","volume":"11","author":"DK Gardner","year":"1999","unstructured":"Gardner, D. K. & Schoolcraft, W. B. Culture and transfer of human blastocysts. Curr. Opin. Obstet. Gynecol. 11, 307\u2013311 (1999).","journal-title":"Curr. Opin. Obstet. Gynecol."},{"key":"31013_CR19","doi-asserted-by":"crossref","unstructured":"Takuya Akiba, S. S., Yanase, T., Ohta, T. & Koyama, M. Optuna: A Next-generation Hyperparameter Optimization Framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD \u201819). Association for Computing Machinery, New York, NY, USA, 2623\u20132631 (2019).","DOI":"10.1145\/3292500.3330701"},{"key":"31013_CR20","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1162\/NECO_a_00084","volume":"23","author":"J Bergstra","year":"2011","unstructured":"Bergstra, J., Bengio, Y. & Louradour, J. Suitability of V1 energy models for object classification. Neural Comput. 23, 774\u2013790 (2011).","journal-title":"Neural Comput."},{"key":"31013_CR21","unstructured":"Lundberg, S. M. & L.S.-I. A unified approach to interpreting model predictions. Proceedings of the 31st international conference on neural information processing systems; Long Beach, California, USA: Curran Associates Inc., 4768\u20134777 (2017)."},{"key":"31013_CR22","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","volume":"3","author":"WJ Youden","year":"1950","unstructured":"Youden, W. J. Index for rating diagnostic tests. Cancer 3, 32\u201335 (1950).","journal-title":"Cancer"},{"key":"31013_CR23","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1186\/s12967-019-2062-5","volume":"17","author":"J Qiu","year":"2019","unstructured":"Qiu, J., Li, P., Dong, M., Xin, X. & Tan, J. Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method. J. Transl Med. 17, 317 (2019).","journal-title":"J. Transl Med."},{"key":"31013_CR24","doi-asserted-by":"publisher","first-page":"2972","DOI":"10.1093\/humrep\/det315","volume":"28","author":"LL van Loendersloot","year":"2013","unstructured":"van Loendersloot, L. L., van Wely, M., Repping, S., Bossuyt, P. M. & van der Veen, F. Individualized decision-making in IVF: calculating the chances of pregnancy. Hum. Reprod. 28, 2972\u20132980 (2013).","journal-title":"Hum. Reprod."},{"key":"31013_CR25","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.ejogrb.2020.06.063","volume":"252","author":"N Balachandren","year":"2020","unstructured":"Balachandren, N. et al. Ovarian reserve as a predictor of cumulative live birth. Eur. J. Obstet. Gynecol. Reprod. Biol. 252, 273\u2013277 (2020).","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"31013_CR26","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1093\/humrep\/dey263","volume":"33","author":"JA Leijdekkers","year":"2018","unstructured":"Leijdekkers, J. A. et al. Predicting the cumulative chance of live birth over multiple complete cycles of in vitro fertilization: an external validation study. Hum. Reprod. 33, 1684\u20131695 (2018).","journal-title":"Hum. Reprod."},{"key":"31013_CR27","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.ejogrb.2018.04.039","volume":"225","author":"SSE Alson","year":"2018","unstructured":"Alson, S. S. E., Bungum, L. J., Giwercman, A. & Henic, E. Anti-mullerian hormone levels are associated with live birth rates in ART, but the predictive ability of anti-mullerian hormone is modest. Eur. J. Obstet. Gynecol. Reprod. Biol. 225, 199\u2013204 (2018).","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"31013_CR28","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1210\/jc.2012-3676","volume":"98","author":"T Brodin","year":"2013","unstructured":"Brodin, T., Hadziosmanovic, N., Berglund, L., Olovsson, M. & Holte, J. Antimullerian hormone levels are strongly associated with live-birth rates after assisted reproduction. J. Clin. Endocrinol. Metab. 98, 1107\u20131114 (2013).","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"31013_CR29","doi-asserted-by":"publisher","first-page":"103890","DOI":"10.1016\/j.rbmo.2024.103890","volume":"49","author":"P Sokol","year":"2024","unstructured":"Sokol, P. et al. Developing and validating a prediction model of live birth following single vitrified-warmed blastocyst transfer. Reprod. Biomed. Online. 49, 103890 (2024).","journal-title":"Reprod. Biomed. Online"},{"key":"31013_CR30","doi-asserted-by":"crossref","unstructured":"Vaegter, K. K. et al., Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF\/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF\/ICSI single-embryo transfers. Fertil Steril 107, 641\u2013648 e642 (2017).","DOI":"10.1016\/j.fertnstert.2016.12.005"},{"key":"31013_CR31","doi-asserted-by":"publisher","first-page":"7216","DOI":"10.1038\/s41598-022-10902-9","volume":"12","author":"A Mehrjerd","year":"2022","unstructured":"Mehrjerd, A., Rezaei, H., Eslami, S. & Ratna, M. B. Khadem Ghaebi, N. Internal validation and comparison of predictive models to determine success rate of infertility treatments: a retrospective study of 2485 cycles. Sci. Rep. 12, 7216 (2022).","journal-title":"Sci. Rep."},{"key":"31013_CR32","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1093\/humrep\/dep474","volume":"25","author":"I Brosens","year":"2010","unstructured":"Brosens, I. et al. The enigmatic uterine junctional zone: the missing link between reproductive disorders and major obstetrical disorders? Hum. Reprod. 25, 569\u2013574 (2010).","journal-title":"Hum. Reprod."},{"key":"31013_CR33","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.bpobgyn.2006.02.001","volume":"20","author":"L Fusi","year":"2006","unstructured":"Fusi, L., Cloke, B. & Brosens, J. J. The uterine junctional zone. Best Pract. Res. Clin. Obstet. Gynaecol. 20, 479\u2013491 (2006).","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"key":"31013_CR34","doi-asserted-by":"publisher","first-page":"1246819","DOI":"10.3389\/fendo.2023.1246819","volume":"14","author":"S Wang","year":"2023","unstructured":"Wang, S. & Duan, H. The role of the junctional zone in the management of adenomyosis with infertility. Front. Endocrinol. (Lausanne). 14, 1246819 (2023).","journal-title":"Front. Endocrinol. (Lausanne)"},{"key":"31013_CR35","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1097\/OGX.0000000000000346","volume":"71","author":"T Harada","year":"2016","unstructured":"Harada, T. et al. The impact of adenomyosis on women\u2019s fertility. Obstet. Gynecol. Surv. 71, 557\u2013568 (2016).","journal-title":"Obstet. Gynecol. Surv."},{"key":"31013_CR36","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1016\/j.fertnstert.2023.06.004","volume":"120","author":"P Humaidan","year":"2023","unstructured":"Humaidan, P., Garcia Velasco, J. A. & Cozzolino, M. Local intraendometrial Estrogen biosynthesis leading to progesterone resistance impacts implantation in adenomyosis and endometriosis. Fertil. Steril. 120, 927 (2023).","journal-title":"Fertil. Steril."},{"key":"31013_CR37","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.1093\/oxfordjournals.humrep.a019435","volume":"11","author":"G Leyendecker","year":"1996","unstructured":"Leyendecker, G., Kunz, G., Wildt, L., Beil, D. & Deininger, H. Uterine hyperperistalsis and dysperistalsis as dysfunctions of the mechanism of rapid sperm transport in patients with endometriosis and infertility. Hum. Reprod. 11, 1542\u20131551 (1996).","journal-title":"Hum. Reprod."},{"key":"31013_CR38","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1007\/s43032-021-00679-z","volume":"28","author":"L Buggio","year":"2021","unstructured":"Buggio, L., Dridi, D., Barbara, G. & Adenomyosis Impact on fertility and obstetric outcomes. Reprod. Sci. 28, 3081\u20133084 (2021).","journal-title":"Reprod. Sci."},{"key":"31013_CR39","first-page":"480","volume":"3","author":"M Cozzolino","year":"2023","unstructured":"Cozzolino, M. et al. Impact of adenomyosis on in vitro fertilization outcomes in women undergoing donor oocyte transfers: a prospective observational study. Fertil. Steril. 3, 480\u2013488 (2023).","journal-title":"Fertil. Steril."},{"key":"31013_CR40","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1002\/jum.14735","volume":"38","author":"CK Rasmussen","year":"2019","unstructured":"Rasmussen, C. K., Hansen, E. S. & Dueholm, M. Inter-rater agreement in the diagnosis of adenomyosis by 2- and 3-dimensional transvaginal ultrasonography. J. Ultrasound Med. 38, 657\u2013666 (2019).","journal-title":"J. Ultrasound Med."},{"key":"31013_CR41","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1002\/uog.10133","volume":"39","author":"J Naftalin","year":"2012","unstructured":"Naftalin, J. et al. Inter- and intraobserver variability in three-dimensional ultrasound assessment of the endometrial-myometrial junction and factors affecting its visualization. Ultrasound Obstet. Gynecol. 39, 587\u2013591 (2012).","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"31013_CR42","doi-asserted-by":"publisher","first-page":"i5735","DOI":"10.1136\/bmj.i5735","volume":"355","author":"DJ McLernon","year":"2016","unstructured":"McLernon, D. J., Steyerberg, E. W., Velde, T., Lee, E. R., Bhattacharya, S. & A.J. & Predicting the chances of a live birth after one or more complete cycles of in vitro fertilisation: population based study of linked cycle data from 113 873 women. BMJ 355, i5735 (2016).","journal-title":"BMJ"},{"key":"31013_CR43","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.fertnstert.2013.11.004","volume":"101","author":"JM Franasiak","year":"2014","unstructured":"Franasiak, J. M. et al. The nature of aneuploidy with increasing age of the female partner: a review of 15,169 consecutive trophectoderm biopsies evaluated with comprehensive chromosomal screening. Fertil. Steril. 101, 656\u2013663e651 (2014).","journal-title":"Fertil. Steril."},{"key":"31013_CR44","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1111\/jog.12635","volume":"41","author":"AI Guzel","year":"2015","unstructured":"Guzel, A. I. et al. Risk factors for adenomyosis, leiomyoma and concurrent adenomyosis and leiomyoma. J. Obstet. Gynaecol. Res. 41, 932\u2013937 (2015).","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"31013_CR45","doi-asserted-by":"crossref","first-page":"995","DOI":"10.18502\/ijrm.v21i12.15038","volume":"21","author":"A Mehrjerd","year":"2023","unstructured":"Mehrjerd, A., Rezaei, H. & Eslami, S. Khadem Ghaebi, N. A hybrid feature selection algorithm to determine effective factors in predictive model of success rate for in vitro fertilization\/intracytoplasmic sperm injection treatment: A cross-sectional study. Int. J. Reprod. Biomed. 21, 995\u20131012 (2023).","journal-title":"Int. J. Reprod. Biomed."},{"key":"31013_CR46","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1016\/j.fertnstert.2014.05.020","volume":"102","author":"B Luke","year":"2014","unstructured":"Luke, B. et al. A prediction model for live birth and multiple births within the first three cycles of assisted reproductive technology. Fertil. Steril. 102, 744\u2013752 (2014).","journal-title":"Fertil. Steril."},{"key":"31013_CR47","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.jare.2013.05.002","volume":"5","author":"L van Loendersloot","year":"2014","unstructured":"van Loendersloot, L., Repping, S., Bossuyt, P. M., van der Veen, F. & van Wely, M. Prediction models in in vitro fertilization; where are we? A mini review. J. Adv. Res. 5, 295\u2013301 (2014).","journal-title":"J. Adv. Res."},{"key":"31013_CR48","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.17715463","author":"O Bj\u00f6rnsson","year":"2025","unstructured":"Bj\u00f6rnsson, O. & Alson, S. ivf_lbr_prediction: machine learning prediction of live birth after IVF using adenomyosis features (v1.0.0). Zenodo https:\/\/doi.org\/10.5281\/zenodo.17715463 (2025).","journal-title":"Zenodo"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-31013-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-31013-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-31013-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:26:59Z","timestamp":1781630819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-31013-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,31]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["31013"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-31013-1","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,31]]},"assertion":[{"value":"14 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"4324"}}