Frontiers in endocrinology · 2026
Stage-specific machine learning prediction of cumulative live birth in women with diminished ovarian reserve.
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Liu Lidan, Liu Bo, Huang Qianyi, Qin Lang, Jiang Li, Wu Huimei
Аннотация
BACKGROUND: 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.
METHODS: We developed and validated stage-specific machine learning models for predicting cumulative live birth per oocyte retrieval cycle-encompassing all fresh and frozen embryo transfers-in 1,234 cycles among women with DOR (AMH ≤1.1 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.
RESULTS: 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 0.704-0.810) for cumulative live birth prediction. Post-stimulation markers conferred negligible incremental value (Stage 2 AUC 0.755, ΔAUC = -0.004). Embryological parameters at pre-transfer substantially enhanced accuracy (Stage 3 AUC 0.793, ΔAUC = +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 (<30%) despite competitive AUCs (0.71-0.77). 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.
CONCLUSIONS: 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.
METHODS: We developed and validated stage-specific machine learning models for predicting cumulative live birth per oocyte retrieval cycle-encompassing all fresh and frozen embryo transfers-in 1,234 cycles among women with DOR (AMH ≤1.1 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.
RESULTS: 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 0.704-0.810) for cumulative live birth prediction. Post-stimulation markers conferred negligible incremental value (Stage 2 AUC 0.755, ΔAUC = -0.004). Embryological parameters at pre-transfer substantially enhanced accuracy (Stage 3 AUC 0.793, ΔAUC = +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 (<30%) despite competitive AUCs (0.71-0.77). 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.
CONCLUSIONS: 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.