Advanced KPI framework for IVF pregnancy prediction models in IVF protocols

Abstract

Abstract The utilization of neural networks in assisted reproductive technology is essential due to their ability to process complex and multidimensional data inherent in IVF procedures, offering opportunities for clinical outcome prediction, personalized treatment implementation, and overall advancement in fertility treatment. The aim of this study was to develop a novel approach to IVF laboratory data analysis, employing deep neural networks to predict the likelihood of clinical pregnancy occurrence within an individual treatment cycle, integrating both key performance indicators and clinical data. We conducted a retrospective analysis spanning 11 years, encompassing 8732 treatment cycles, to extract the most relevant features to our goal and train the model. Internal validation was performed on 1600 preimplantation genetic testing for aneuploidy embryo transfers, while external was conducted across two independent clinics (over 10,000 cases). Leveraging recurrent neural networks, our model demonstrates high accuracy in predicting the likelihood of clinical pregnancy within specific IVF treatment cycles (AUC = 0.68–0.86; test accuracy = 0.78, F1 score = 0.71, sensitivity = 0.62; specificity = 0.86) comparable to time-lapse system but with a simpler approach. Our model facilitates both retrospective analysis of outcomes and prospective evaluation of clinical pregnancy chances, thus presenting a promising avenue for quality management programs and promotes their realization in medical centers.

Keywords

• Deep neural networks
• IVF laboratory data analysis
• Clinical pregnancy prediction
• Personalized treatment
• ART quality management