The field of IVF:
As a result of infertility, the use of In Vitro Fertilization (IVF) has become a booming industry by selecting viable embryos to grow and subsequently implant. The ‘scoring’ of embryonic quality uses numerous metrics, such as low percentages of cell fragments, absence of multi-nucleation and the number of blastomeres. Building a prediction model using AI has been a viable solution in the past years, while focusing on integrating the multiple factors that contribute to the implantation score of an embryo. This consists of monitoring the embryo through its developmental phases while keeping track of certain metrics to determine the implantation score. Only then can selected embryos be transplanted based on the highest scores.
Hence the development of a proprietary comprehensive algorithm by the Pytri team based on a Convolutional Neural Network to develop a selection tool that can be implemented in the industry. Achieving these results would require the collection of a large dataset through our outreach team, following the curation and annotations of the various metrics required. The proficiency of the team in both the biological and computational fields will allow the proper integration of a solution through our platform in the upcoming months.