Bayesian Network Modeling of IVF Blastocyst Score Prediction

Abstract

Embryo transfer may be performed at cleavage stage (on day 2-3) or at blastocyst stage (on day 5) in In-Vitro Fertilization (IVF) treatment. Elective single embryo transfer at blastocyst stage increases the pregnancy probability and reduces the number of multiple pregnancies. However, the extended culture of embryos in the laboratory may result in transfer cancelation if no high quality blastocyst develops by day 5. Predicting the blastocyst score of individual embryos may help physicians to decide whether or not to further culture the embryos in the laboratory. In this paper, we use Bayesian networks for predicting the blastocyst score by modeling the morphological evolution of IVF embryos. We propose a weighted nearest neighbor approach to adjust the frequency estimates in the conditional probability table. Experimental results show that the proposed method significantly increases the accuracy and reduces false positive rates in IVF data in comparison to the frequency estimate method. Our proposed model can also predict low quality blastocyst development with a 77.3% True Negative rate. Using this model can help preventing developmental failures of embryos during IVF treatment.