P-179 Aneuploid embryos as a proposal for improving Artificial Intelligence performance

Abstract

Abstract Study question Could we improve the performance of Machine Learning algorithms by using aneuploid embryos instead of non-implanted embryos as the contrary reference to Live-Birth embryos? Summary answer Machine Learning (ML) algorithms results were improved when aneuploid embryos were taken into consideration. What is known already Artificial Intelligence (AI) techniques have been focusing on Deep Learning (DL) image recognition although several Machine Learning algorithms are also suitable for morphokinetic analysis. Traditional Live-Birth prediction labelling could have included in the outcome variable a certain number of potentially evolving embryos with a negative result due to factors unrelated to the embryo such as endometrial receptivity. According to this, mislabelling could lead to distorted predictions and decreased AI performance. Aneuploid diagnosis could be useful for potential prediction labelling despite some aneuploid embryos could still be viable as a result of mosaicism or non-concordance misdiagnosis. Study design, size, duration Retrospective analysis of morphokinetic data and clinical outcomes of 343 embryos in a single IVF unit between 2014 and 2021. Participants/materials, setting, methods Two datasets were prepared and used for training and testing (V-Fold Cross-Validation) by three ML algorithms: eXtreme Gradient Boosting (XGB), k-Nearest Neighbor (kNN) and Random Forest (rF). Both datasets shared 117 Live-Birth Embryos. “Dataset A” included 123 non-implanted embryos while “Dataset B” comprised 103 aneuploid embryos which were kept vitrified at the blastocyst stage. The prediction power for each dataset model was measured using the area under the curve (AUC) and its confusion matrix’s metrics. Main results and the role of chance All metrics for each Machine Learning algorithm analysed were higher in the dataset including aneuploid embryos. The AUC for “Dataset A” did not reach the value of 0.6 (XGB = 0.540, kNN = 0.500, rF = 0.590) while AUC values for “Dataset B” surpassed 0.7 (XGB = 0.722, kNN = 0.718, rF = 0.740). According to this, different morphokinetic patterns were detected by Machine Learning algorithms. Algorithms’ minor performance with non-implanted embryos may be due to an increased Label Noise effect, suggesting that including aneuploid embryos could be more appropriate when building predictive algorithms for embryo viability. Limitations, reasons for caution Although the sample size was larger than the minimum recommended for training Machine Learning algorithms, this study should be replicated with a higher number of embryos. Mosaic embryos should be included in further and deeper analysis. Wider implications of the findings This study was the first part of a global project based on AI and time-lapse data. Implantation and Live-Birth Rate will be calculated for each predicted level. Further studies are needed to confirm if other AI techniques such as DL could also improve their performance. Trial registration number not applicable