O-131 A novel approach to predicting the potential of a blastocyst using untargeted spectral analysis of spent culture medium and machine learning

Abstract

Abstract Study question Is it possible to effectively predict negative outcomes of intrauterine embryo transfer using modified untargeted Raman spectroscopy of the spent culture medium? Summary answer The proposed spectroscopic approach, combined with machine learning methods, allows identifying a group of embryos that are almost unable to implant and develop ongoing pregnancy. What is known already Embryo deselection is usually based on the karyotype analysis of the embryo and originates from the assumption that an abnormal PGT result is associated with negative transfer outcomes. Embryo selection methods aimed at identifying the most capable for implantation embryo are based on morphological, morphometric, and metabolic approaches. However, due to the dependence of the phenomenon (pregnancy) on several factors, predicting the outcome of the transfer solely through variables derived from the embryo does not correspond to modern views on implantation. At the same time, predicting negative transfer outcomes through the information originating from the embryo appears quite rational. Study design, size, duration 424 samples of spent culture medium used, collected from microdrops with individually cultured blastocyst from the internal institutional biosamples repository. Medium samples were collected from July 1, 2021, to June 1, 2023. The class attribute for each sample was the absence or presence of ongoing pregnancy at the 12-14 week of gestational age. The positive class (ongoing pregnancy) proportion in the presented dataset is 33%, and the negative (no ongoing pregnancy) class proportion is 67%. Participants/materials, setting, methods The study material was spent culture medium from blastocysts with known transfer outcomes by the end of first trimester. All media samples were stored at -86 °С until the moment of spectroscopy. Before the study, samples were thawed at room temperature for 15 minutes. Untargeted Raman spectroscopy was done on a signal amplification substrate using a green laser (532 nm). The architecture selection and machine learning model training were performed using the Python library mcfly. Main results and the role of chance After building the model the following results for validation and test sets were obtained: So it is possible to identify a group of embryos that are almost incapable of producing ongoing pregnancy. This group constitutes about 40% of the total number of negative outcomes. The error of determining an embryo as being incapable for implantation and normal pregnancy development is about 6%, what is relatively low number. Limitations, reasons for caution The presented data are not multicenter; they were obtained from samples from one laboratory and using a limited set (only one manufacturer) of culture media. This may lead to reduced or nonexistent relevance of the model for data from other sources and conditions. Wider implications of the findings It is necessary to check the proposed method on an additional set of culture media from other manufactures and laboratories. This may lead to refining the parameters of the built neural network. The assumption of significant clinical value of the developed model needs to be checked through prospective testing. Trial registration number fpc-2023-ivf-01