Prediction of blastocyst formation based on fusion of morphokinetic and morphological features

The transition from a highly subjective morphological assessment to time-lapse imaging improves the accuracy of predicting embryonic developmental potential. In actual operations, embryos are cultured for 2–3 days in a time-lapse monitoring system before being transferred to recipients. However, most existing prediction models require videos or images spanning a five-day period. Therefore, it is necessary to develop a method that accurately predicts blastocyst formation given input data spanning only 2–3 days. In this study, we propose a method for predicting blastocyst formation using early morphokinetic and morphological parameters prior to the five-cell stage. We employed a YOLOv5 pretrained deep-learning network to recognize the first four-cell stages for the accurate extraction of morphokinetic parameters and used these parameters as inputs to construct four long short-term memory-based morphokinetic models for blastocyst formation prediction, obtaining the best area-under-the-curve (AUC) value of 0.7297 0.669–0.884. We then extracted the three frames before and after the t1–t4 time points and calculated the image entropy and gray-level co-occurrence matrix entropy as morphological features to build a prediction model. This model was subsequently fused with the morphokinetic model, and an AUC of 0.8325 0.7601–0.9067 was achieved. Our results have implications for automatic embryo screening given information on early embryonic development.