{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:09:54Z","timestamp":1774512594675,"version":"3.50.1"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Ovarian endometriomas impair ovarian reserve and fertility in women of reproductive age. Ethanol sclerotherapy is a fertility-preserving alternative to surgery. Nonetheless, predicting cumulative live birth rates after\n                      <jats:italic>in vitro<\/jats:italic>\n                      fertilization remains challenging. This study aimed to develop and validate a machine learning model for predicting the cumulative live birth rate in women with endometriomas who underwent alcohol sclerotherapy followed by assisted reproduction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective cohort study included 194 patients with ovarian endometriomas who underwent ultrasound-guided ethanol sclerotherapy before\n                      <jats:italic>in vitro<\/jats:italic>\n                      fertilization or intracytoplasmic sperm injection cycles between January 2020 and December 2024\u00a0at our institution. Patients were allocated to the training (135 patients, 70%) and validation (59 patients, 30%) groups. Feature selection used univariate logistic regression (p &amp;lt; 0.10) to identify 19 predictors, which were refined using the Boruta, Recursive Feature Elimination, and maximum relevance minimum redundancy algorithms. Features identified by all methods were selected as the final predictors. Four machine learning algorithms (Decision Tree, Random Forest, Extreme Gradient Boosting, Support Vector Machine) were compared using discrimination, calibration, and utility metrics. SHapley Additive exPlanations analysis was used to interpret the model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The cumulative live birth rate was 50.0% (97\/194). Five predictors were identified: antral follicle count, progesterone level on gonadotropin starting day, downregulation, cyst diameter, and previous live birth history. The Extreme Gradient Boosting model showed optimal performance, with an AUC of 0.830 (95% confidence interval: 0.719\u20130.941), sensitivity of 0.783, specificity of 0.750, and Brier score of 0.176. SHapley analysis revealed\u200b that a higher antral follicle count and downregulation positively impacted birth prediction, whereas elevated progesterone levels and larger cyst diameters had negative effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed an explainable Extreme Gradient Boosting model for predicting cumulative live birth rates in women with ovarian endometriomas after ethanol sclerotherapy and assisted reproductive technology. SHapley Additive exPlanations analysis identified key predictors and revealed their non-linear contributions to outcomes, providing transparent explanations for predictions. This interpretable machine learning approach offers a clinical decision-support tool for patient counseling and treatment optimization, advancing beyond traditional methods in capturing reproductive outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2026.1742816","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:35:53Z","timestamp":1774506953000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying key determinants of cumulative live birth in women with ovarian endometrioma undergoing ethanol sclerotherapy followed by in vitro fertilization or intracytoplasmic sperm injection: an interpretable machine learning analysis"],"prefix":"10.3389","volume":"14","author":[{"given":"Bowen","family":"Liu","sequence":"first","affiliation":[{"name":"Guangdong Cardiovascular Institute, Guangdong Provincial People\u2019s Hospital, Ganzhou Hospital, Guangdong Academy of Medical Sciences","place":["Guangzhou, China"]},{"name":"Guangdong Cardiovascular Institute, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Nanfang Hospital, Southern Medical University","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yamei","family":"Li","sequence":"additional","affiliation":[{"name":"Reproductive Medicine Center, Shenzhen Hengsheng Hospital","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yudong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Nanfang Hospital, Southern Medical University","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weifen","family":"Deng","sequence":"additional","affiliation":[{"name":"Reproductive Medicine Center, Shenzhen Hengsheng Hospital","place":["Shenzhen, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhua","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Nanfang Hospital, Southern Medical University","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"B1","first-page":"179","article-title":"Assessing the efficacy of aspiration and ethanol injection in recurrent endometrioma before IVF cycle: a randomized clinical trial","volume":"11","author":"Aflatoonian","year":"2013","journal-title":"Iran. J. Reprod. Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1038\/s41598-021-04637-2","article-title":"Machine learning algorithms as new screening approach for patients with endometriosis","volume":"12","author":"Bendifallah","year":"2022","journal-title":"Sci. Rep."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1007\/s43032-024-01490-2","article-title":"Predictive role of progesterone levels for IVF outcome in different phases of controlled ovarian stimulation for patients with and without endometriosis: expert view","volume":"31","author":"Bila","year":"2024","journal-title":"Reprod. Sci."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1020827","DOI":"10.3389\/fendo.2022.1020827","article-title":"Endometriosis-associated infertility: from pathophysiology to tailored treatment","volume":"13","author":"Bonavina","year":"2022","journal-title":"Front. Endocrinol. (Lausanne)"},{"key":"B5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s12958-020-00571-6","article-title":"The effectiveness of different down-regulating protocols on in vitro fertilization-embryo transfer in endometriosis: a meta-analysis","volume":"18","author":"Cao","year":"2020","journal-title":"Reprod. Biol. Endocrinol."},{"key":"B6","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J. Clin. Epidemiol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3389\/fendo.2018.00327","article-title":"Impact of maternal age on oocyte and embryo competence","volume":"9","author":"Cimadomo","year":"2018","journal-title":"Front. Endocrinol. (Lausanne)"},{"key":"B8","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.fertnstert.2017.05.015","article-title":"Sclerotherapy in the management of ovarian endometrioma: systematic review and meta-analysis","volume":"108","author":"Cohen","year":"2017","journal-title":"Fertil. Steril."},{"key":"B9","doi-asserted-by":"publisher","first-page":"g7594","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins,","year":"2015","journal-title":"Bmj"},{"key":"B10","doi-asserted-by":"publisher","first-page":"5324","DOI":"10.3390\/jcm12165324","article-title":"Surgical management of ovarian endometrioma: impact on ovarian reserve parameters and reproductive outcomes","volume":"12","author":"Daniilidis","year":"2023","journal-title":"J. Clin. Med."},{"key":"B11","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/s2589-7500(21)00208-9","article-title":"The false hope of current approaches to explainable artificial intelligence in health care","volume":"3","author":"Ghassemi","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"B12","doi-asserted-by":"publisher","first-page":"685","DOI":"10.3390\/biomedicines11030685","article-title":"Sample size analysis for machine learning clinical validation studies","volume":"11","author":"Goldenholz","year":"2023","journal-title":"Biomedicines"},{"key":"B13","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1093\/humupd\/dmv035","article-title":"The impact of endometrioma on IVF\/ICSI outcomes: a systematic review and meta-analysis","volume":"21","author":"Hamdan","year":"2015","journal-title":"Hum. Reprod. Update"},{"key":"B14","doi-asserted-by":"publisher","first-page":"3114","DOI":"10.1038\/s41591-024-03166-5","article-title":"Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial","volume":"30","author":"Illingworth","year":"2024","journal-title":"Nat. Med."},{"key":"B15","doi-asserted-by":"publisher","first-page":"2018","DOI":"10.1093\/humrep\/dey284","article-title":"Perinatal outcomes of singleton live births with and without vanishing twin following transfer of multiple embryos: analysis of 113 784 singleton live births","volume":"33","author":"Kamath","year":"2018","journal-title":"Hum. Reprod."},{"key":"B16","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s10815-022-02403-5","article-title":"Endometrioma and assisted reproductive technology: a review","volume":"39","author":"Kheil","year":"2022","journal-title":"J. Assist. Reprod. Genet."},{"key":"B17","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1093\/humupd\/dmp036","article-title":"Anti-mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART)","volume":"16","author":"La Marca","year":"2010","journal-title":"Hum. Reprod. Update"},{"key":"B18","doi-asserted-by":"publisher","first-page":"104840","DOI":"10.1016\/j.rbmo.2025.104840","article-title":"Ovarian reserve and IVF outcomes after ethanol ovarian sclerotherapy in women with endometrioma: a systematic review and meta-analysis","volume":"51","author":"Lavadia","year":"2025","journal-title":"Reprod. Biomed. Online"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1016\/j.fertnstert.2020.06.006","article-title":"Artificial intelligence in in vitro fertilization: a computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization","volume":"114","author":"Letterie","year":"2020","journal-title":"Fertil. Steril."},{"key":"B20","doi-asserted-by":"publisher","first-page":"30826","DOI":"10.1038\/s41598-024-81539-z","article-title":"Progesterone level in assisted reproductive technology: a systematic review and meta-analysis","volume":"14","author":"Lim","year":"2024","journal-title":"Sci. Rep."},{"key":"B21","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"B22","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1089\/jwh.2017.6397","article-title":"Quality of life in women with endometriosis: an integrative review","volume":"27","author":"Marinho","year":"2018","journal-title":"J. Womens Health (Larchmt)"},{"key":"B23","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1093\/humrep\/dev336","article-title":"Cumulative live birth rates after one or more complete cycles of IVF: a population-based study of linked cycle data from 178,898 women","volume":"31","author":"McLernon","year":"2016","journal-title":"Hum. Reprod."},{"key":"B24","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.fertnstert.2014.12.101","article-title":"Second surgery for recurrent endometriomas is more harmful to healthy ovarian tissue and ovarian reserve than first surgery","volume":"103","author":"Muzii","year":"2015","journal-title":"Fertil. Steril."},{"key":"B25","doi-asserted-by":"publisher","first-page":"102307","DOI":"10.1016\/j.bpobgyn.2022.102307","article-title":"Association between the number of oocytes and cumulative live birth rate: a systematic review","volume":"87","author":"Neves","year":"2023","journal-title":"Best. Pract. Res. Clin. Obstet. Gynaecol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"102903","DOI":"10.1016\/j.jogoh.2024.102903","article-title":"Artificial intelligence in in-vitro fertilization (IVF): a new era of precision and personalization in fertility treatments","volume":"54","author":"Olawade","year":"2025","journal-title":"J. Gynecol. Obstet. Hum. Reprod."},{"key":"B27","doi-asserted-by":"publisher","first-page":"e81568","DOI":"10.7759\/cureus.81568","article-title":"Artificial intelligence in assisted reproductive technology: a new era in fertility treatment","volume":"17","author":"Orovou","year":"2025","journal-title":"Cureus"},{"key":"B28","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/j.fertnstert.2018.04.039","article-title":"Cumulative live birth rates according to the number of oocytes retrieved after the first ovarian stimulation for in vitro fertilization\/intracytoplasmic sperm injection: a multicenter multinational analysis including \u223c15,000 women","volume":"110","author":"Polyzos","year":"2018","journal-title":"Fertil. Steril."},{"key":"B29","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1016\/j.fertnstert.2020.09.134","article-title":"Testing and interpreting measures of ovarian reserve: a committee opinion","volume":"114","year":"2020","journal-title":"Fertil. Steril."},{"key":"B30","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1093\/humupd\/dmy033","article-title":"Fresh versus elective frozen embryo transfer in IVF\/ICSI cycles: a systematic review and meta-analysis of reproductive outcomes","volume":"25","author":"Roque","year":"2019","journal-title":"Hum. Reprod. Update"},{"key":"B31","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s13048-017-0341-4","article-title":"Is the oocyte quality affected by endometriosis? A review of the literature","volume":"10","author":"Sanchez","year":"2017","journal-title":"J. Ovarian Res."},{"key":"B32","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1056\/NEJMoa1705334","article-title":"Transfer of fresh versus frozen embryos in ovulatory women","volume":"378","author":"Shi","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"B33","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1186\/s12958-023-01157-8","article-title":"Endometriosis and IVF treatment outcomes: unpacking the process","volume":"21","author":"Somigliana","year":"2023","journal-title":"Reprod. Biol. Endocrinol."},{"key":"B34","doi-asserted-by":"publisher","first-page":"1768","DOI":"10.1093\/humrep\/der106","article-title":"Association between the number of eggs and live birth in IVF treatment: an analysis of 400 135 treatment cycles","volume":"26","author":"Sunkara","year":"2011","journal-title":"Hum. Reprod."},{"key":"B35","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.ajog.2017.02.027","article-title":"Ovarian reserve testing: a user's guide","volume":"217","author":"Tal","year":"2017","journal-title":"Am. J. Obstet. Gynecol."},{"key":"B36","doi-asserted-by":"publisher","first-page":"102541","DOI":"10.1016\/j.jogoh.2023.102541","article-title":"Efficacy of long-term pituitary down-regulation pretreatment prior to in vitro fertilization in infertile patients with endometriosis: a meta-analysis","volume":"52","author":"Tian","year":"2023","journal-title":"J. Gynecol. Obstet. Hum. Reprod."},{"key":"B37","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","article-title":"Calibration: the achilles heel of predictive analytics","volume":"17","author":"Van Calster","year":"2019","journal-title":"BMC Med."},{"key":"B38","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1093\/humupd\/dmt014","article-title":"Progesterone elevation and probability of pregnancy after IVF: a systematic review and meta-analysis of over 60 000 cycles","volume":"19","author":"Venetis","year":"2013","journal-title":"Hum. Reprod. Update"},{"key":"B39","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1093\/humrep\/den379","article-title":"Surgery for endometriosis-associated infertility: a pragmatic approach","volume":"24","author":"Vercellini","year":"2009","journal-title":"Hum. Reprod."},{"key":"B40","doi-asserted-by":"publisher","first-page":"739228","DOI":"10.3389\/fendo.2021.739228","article-title":"Ovarian endometrioma negatively impacts oocyte quality and quantity but not pregnancy outcomes in women undergoing IVF\/ICSI treatment: a retrospective cohort study","volume":"12","author":"Wu","year":"2021","journal-title":"Front. Endocrinol. (Lausanne)"},{"key":"B41","doi-asserted-by":"publisher","first-page":"5927","DOI":"10.1097\/JS9.0000000000002647","article-title":"Global, regional, and national burden of endometriosis among women of childbearing age from 1990 to 2021: a cross-sectional analysis from the 2021 global burden of disease study","volume":"111","author":"Xu","year":"2025","journal-title":"Int. J. Surg."},{"key":"B42","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.fertnstert.2020.09.157","article-title":"Artificial intelligence in human in vitro fertilization and embryology","volume":"114","author":"Zaninovic","year":"2020","journal-title":"Fertil. Steril."},{"key":"B43","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1093\/humupd\/dmz046","article-title":"Adjuvant treatment strategies in ovarian stimulation for poor responders undergoing IVF: a systematic review and network meta-analysis","volume":"26","author":"Zhang","year":"2020","journal-title":"Hum. Reprod. Update"},{"key":"B44","doi-asserted-by":"publisher","first-page":"3853","DOI":"10.1007\/s10815-025-03677-1","article-title":"Development and validation of a machine learning-based predictive model for live birth outcomes following fresh embryo transfer in patients with endometriosis","volume":"42","author":"Zhu","year":"2025","journal-title":"J. Assist. Reprod. Genet."},{"key":"B45","doi-asserted-by":"publisher","first-page":"1244","DOI":"10.1056\/NEJMra1810764","article-title":"Endometriosis","volume":"382","author":"Zondervan","year":"2020","journal-title":"N. Engl. J. Med."}],"container-title":["Frontiers in Cell and Developmental Biology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2026.1742816\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:35:55Z","timestamp":1774506955000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2026.1742816\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,26]]},"references-count":45,"alternative-id":["10.3389\/fcell.2026.1742816"],"URL":"https:\/\/doi.org\/10.3389\/fcell.2026.1742816","relation":{},"ISSN":["2296-634X"],"issn-type":[{"value":"2296-634X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,26]]},"article-number":"1742816"}}