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 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 ge