Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?

Can models based on artificial intelligence predict embryonic ploidy status or implantation potential of euploid transferred embryos? Can the addition of clinical features into time-lapse monitoring (TLM) parameters as input data improve their predictive performance?

A single academic fertility centre, retrospective cohort study. A total of 773 high-grade euploid and aneuploid blastocysts from 212 patients undergoing preimplantation genetic testing (PGT) between July 2016 and July 2021 were studied for ploidy prediction. Among them, 170 euploid embryos were single-transferred and included for implantation analysis. Five machine learning models and two types of deep learning networks were used to develop the predictive algorithms. The predictive performance was measured using the area under the receiver operating characteristic curve (AUC), in addition to accuracy, precision, recall and F1 score.

The most predictive model for ploidy prediction had an AUC, accuracy, precision, recall and F1 score of 0.70, 0.64, 0.64, 0.50 and 0.56, respectively. The DNN-LSTM model showed the best predictive performance with an AUC of 0.78, accuracy of 0.77, precision of 0.79, recall of 0.86 and F1 score of 0.83. The predictive power was improved after the addition of clinical features for the algorithms in ploidy prediction and implantation prediction.

Our findings emphasize that clinical features can largely improve embryo prediction performance, and their combination with TLM parameters is robust to predict high-grade euploid blastocysts. The models for ploidy prediction, however, were not highly predictive, suggesting they cannot replace preimplantation genetic testing currently.