Neural networks pipeline for quality management in IVF laboratory

Abstract

This study introduces a novel neural network-based pipeline for predicting clinical pregnancy rates in IVF treatments, integrating both clinical and laboratory data. We developed a metamodel combining deep neural networks and Kolmogorov-Arnold networks, leveraging their complementary strengths to enhance predictive accuracy and interpretability. The metamodel achieved robust performance metrics after training and fitting on 11500 clinical cases: accuracy = 0.72, AUC = 0.75, F1 score = 0.60, and Matthews Correlation Coefficient of 0.42. According to morpho-kinetical embryo evaluation, our model’s PRC of 0.66 significantly improves over existing time-lapse systems for pregnancy prediction, demonstrating better handling of imbalanced clinical data. The metamodel’s calibration metrics (Brier score = 0.20, expected calibration error = 0.06, maximum calibration error = 0.12, Hosmer-Lemeshow test p-value = 0.06) indicate robust reliability in predicting clinical pregnancy outcomes. We validated the model’s reproducibility using an independent dataset of 665 treatment cycles, showing close alignment between predicted and actual pregnancy rates (58.9% vs. 59.1%). With the Bayesian method, we proposed a robust framework for integrating historical data with real-time predictions from neural networks, enabling a transition from retrospective to prospective analysis. Our approach extends beyond conventional embryo selection, incorporating post-analytical phase evaluation in the IVF laboratory. This comprehensive framework enables detailed analysis across different patient subpopulations and time periods, facilitating the identification of systemic issues and IVF protocol optimization. The model’s ability to track pregnancy probabilities over time and staff members allows for both outcome prediction and retrospective and prospective assessment of IVF treatment efficacy, providing a data-driven strategy for continuous improvement in assisted reproductive technology.