Enhancing predictive models for egg donation: time to blastocyst hatching and machine learning insights

Abstract

Abstract Background Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorithm developed using a known implantation database of embryos transferred in our egg donation program, including morphokinetic and morphological variables, and recognize the most predictive embryo parameters in order to enhance IVF treatments clinical outcomes. Methods Multicenter retrospective cohort study carried out in 378 egg donor recipients who performed a fresh single embryo transfer during 2021. All treatments were performed by Intracytoplasmic Sperm Injection, using fresh or frozen oocytes. The embryos were cultured in Geri® time-lapse incubators until transfer on day 5. The embryonic morphokinetic events of 378 blastocysts with known implantation and live birth were analyzed. Classical statistical analysis (binary logistic regression) and 10 machine learning algorithms were applied including Multi-Layer Perceptron, Support Vector Machines, k-Nearest Neighbor, Cart and C0.5 Classification Trees, Random Forest (RF), AdaBoost Classification Trees, Stochastic Gradient boost, Bagged CART and eXtrem Gradient Boosting. These algorithms were developed and optimized by maximizing the area under the curve. Results The Random Forest emerged as the most predictive algorithm for implantation (area under the curve, AUC = 0.725, IC 95% [0.6232–0826]). Overall, implantation and miscarriage rates stood at 56.08% and 18.39%, respectively. Overall live birth rate was 41.26%. Significant disparities were observed regarding time to hatching out of the zona pellucida (p = 0.039). The Random Forest algorithm demonstrated good predictive capabilities for live birth (AUC = 0.689, IC 95% [0.5821–0.7921]), but the AdaBoost classification trees proved to be the most predictive model for live birth (AUC = 0.749, IC 95% [0.6522–0.8452]). Other important variables with substantial predictive weight for implantation and live birth were duration of visible pronuclei (DESAPPN-APPN), synchronization of cleavage patterns (T8-T5), duration of compaction (TM-TiCOM), duration of compaction until first sign of cavitation (TiCAV-TM) and time to early compaction (TiCOM). Conclusions This study highlights Random Forest and AdaBoost as the most effective machine learning models in our Known Implantation and Live Birth Database from our egg donation program. Notably, time to blastocyst hatching out of the zona pellucida emerged as a highly reliable parameter significantly influencing our implantation machine learning predictive models. Processes involving syngamy, genomic imprinting during embryo cleavage, and embryo compaction are also influential and could be crucial for implantation and live birth outcomes.

Keywords

• Artificial intelligence
• Machine learning
• Embryo morphokinetic
• Hatching blastocyst
• Implantation and live birth