{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T05:25:26Z","timestamp":1792041926632,"version":"4.4.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Women with diminished ovarian reserve (DOR) experience cumulative live birth (cLBR) rates below 30% following embryo transfer, yet existing prediction tools rely on static baseline parameters and lack interpretability, limiting their clinical utility for counseling about long-term treatment success.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed and validated stage-specific machine learning models for predicting cumulative live birth per oocyte retrieval cycle\u2014encompassing all fresh and frozen embryo transfers\u2014in 1,234 cycles among women with DOR (AMH \u22641.1 ng\/mL) at a single tertiary center. Using Random Forest feature selection and six tree-based algorithms, we constructed models at three decision junctures: baseline (pre-treatment), post-stimulation (trigger day), and pre-transfer. The cohort was randomly split into training (n=863, 70%) and test (n=371, 30%) sets. Class-imbalance mitigation strategies were systematically evaluated given the 22.8% cumulative live birth prevalence. Model interpretability was assessed using Shapley Additive Explanations (SHAP) to quantify feature contributions in clinically meaningful units.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The CatBoost algorithm consistently achieved the highest test-set discrimination across stages. Baseline models (5 features: female age, male age, AMH, BMI, infertility duration) yielded AUC 0.759 (95% CI 0.704\u20130.810) for cumulative live birth prediction. Post-stimulation markers conferred negligible incremental value (Stage 2 AUC 0.755, \u0394AUC = \u22120.004). Embryological parameters at pre-transfer substantially enhanced accuracy (Stage 3 AUC 0.793, \u0394AUC = +0.034 vs baseline), achieving sensitivity 70.6%, specificity 72.4%, and F1-score 0.536. Algorithms without explicit class-balancing exhibited severely depressed sensitivity (&amp;lt;30%) despite competitive AUCs (0.71\u20130.77). SHAP analysis revealed that female age (32.8% of total importance) and embryo quality (29.7%) dominated predictions, with non-linear thresholds at age 37 years and clear stratification across embryo grades.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Embryological parameters substantially enhance cumulative live birth prediction in DOR populations, while ovarian response markers provide minimal added value. Explicit class-imbalance mitigation and interpretable model frameworks are essential for clinically meaningful predictions in reproductive medicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1832301","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:22:11Z","timestamp":1784002931000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Stage-specific machine learning prediction of cumulative live birth in women with diminished ovarian reserve"],"prefix":"10.3389","volume":"17","author":[{"given":"Lidan","family":"Liu","sequence":"first","affiliation":[{"name":"Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianyi","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lang","family":"Qin","sequence":"additional","affiliation":[{"name":"Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Jiang","sequence":"additional","affiliation":[{"name":"Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimei","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University","place":["Nanning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1093\/humrep\/der092","article-title":"ESHRE consensus on the definition of \u201cpoor response\u201d to ovarian stimulation for in vitro fertilization: the Bologna criteria","volume":"26","author":"Ferraretti","year":"2011","journal-title":"Hum Reprod"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fertnstert.2020.09.134","article-title":"Testing and interpreting measures of ovarian reserve: a committee opinion","volume":"114","author":"Penzias","year":"2020","journal-title":"Fertil 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