P-178 Time to hatching: the most predictive morphokinetic parameter for embryo implantation in machine learning algorithms following time-lapse incubation and blastocyst transfer in oocyte-donation programs

Abstract

Abstract Study question Which is the most predictive parameter when machine learning (ML) is applied to a known implantation database (KID) of day 5 embryo transfer database in an egg donation program? Summary answer Time to hatching (tiHB) is the most predictive embryonic parameter when machine learning algorithms were used on reproductive data in an oocyte donation program. What is known already Artificial intelligence is becoming an encouraging tool in medicine, also in ART, where the amount of data generated in the IVF lab has dramatically increase, favored by time-lapse technology. Numerous embryo selections algorithms based on logistic regressions have been developed for predicting blastocyst formation and implantation potential, but with machine learning, we can train algorithms and connect different morphological and morphokinetic embryo parameters with implantation or even live birth embryo potential. The aim of this study was to test machine learning algorithms and to identify predictive embryonic morphokinetic parameters when comparing the different models generated after machine learning analysis. Study design, size, duration Retrospective analysis of 405 embryos in a KID obtained after 392 embryo-transfers (13 double and 379 single-ET) performed in an oocyte donation program in 4 fertility clinics (year 2021). Recipientś average age: 42.2±4.2 years. The embryos were cultured in Global® Total® culture medium in Geri® (Genea Biomedx) time-lapse incubators after ICSI until embryo transfer at blastocyst stage. Only sperm samples >1x106 spermatozoa/ml were included. All parameters were registered by one single trained senior embryologist. Participants/materials, setting, methods Thirty-five variables were initially analyzed: classic morphokinetic markers, time intervals (including total thinning time before hatching: tiHB-tFB and total blastulation time before hatching: tiHB-Tcav) and morphological measurements (blastocyst and inner cell mass diameter 110h post-injection). Eighty percent of the data was used for model training and 20% was reserved for model validation. Twelve supervised and unsupervised predictive machine learning models were developed. The software used to carry out the analysis was SPSS (v20.0) R (4.0.5). Main results and the role of chance The basic characteristics of the embryo population were similar. From the 405 embryos transferred, 216 blastocysts came from vitrified oocytes (53.3%). The implantation rate was 57.03% (231 gestational sacs) and the miscarriage rate was 16.8%. The classification-supervised algorithms applied included binary logistic regression, neural networks, support vector machines, neighborhood-based methods, classification trees, boosting and bagging methods. The algorithms were optimized by minimizing the AUC. Cluster analysis (unsupervised) was also performed. In the 6 best predictive models, the variables with the highest relevance were tiHB (hatching initiation time), tiHB-tFB and tiHB-tcav: variables related to hatching initiation. Furthermore, in the cluster analysis, these three variables appeared grouped in the same cluster. From the blastocysts population that implanted, 57.6% (133/231) were initiating hatching, while from those embryos that did not, only 42.3% (74/174) began the hatching process. Other variables such as the diameter of the transferred blastocyst, which we assumed to be valuable as an objective morphological parameter, did not show a high predictive capacity in the models obtained. Blastocyst average diameter of implanting blastocysts was 157.9±24.9 µm and non-implanting was 153.9±26.1 µm. Limitations, reasons for caution Morphology and morphokinetic parameters require subjective annotation and thus might have intrinsic intra-reader variability. Our findings need to be validated prospectively. Wider implications of the findings Time to blastocyst hatching appears to have significative impact in most ML predictive models. Hatching-related variables seems to have predictive power. Despite numerous variables influencing IVF outcome (intrinsic and extrinsic to embryo development) ML and AI approaches may improve the prioritization of the most viable embryo favoring single embryo transfer. Trial registration number 2021ibmad001