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. [ABSTRACT FROM AUTHOR]