Using machine learning to analyze the lipid profile of culture medium and predict the efficacy of assisted reproductive technologies
20.03.2025
10:58
Russian researchers conducted a study aimed to developed a method for predicting assisted reproductive technologies (ART) outcomes based on analyzing the lipid profile of embryo culture medium on day 5 post-fertilization using gradient boosting (GB), and to identify the lipids that contribute most to this prediction.
A GB model was developed to predict ART outcomes based on lipid profiles of the culture medium. The model achieved 79% accuracy (f1 score: 0.81) in identifying the lipid profiles associated with embryos that resulted in pregnancy. Among the lipids identified, triacylglycerols were found to contribute the most to determining embryo implantation potential.
Analyzing liquid chromatography–mass spectrometry data using GB allows for the identification of different classes of lipids in the embryo culture medium, which can serve as a noninvasive approach to assess embryo quality and implantation potential. This can also facilitate the development of a predictive testing system to determine the effectiveness of ART programs. Additionally, this information enables a more detailed investigation of the mechanisms of gamete damage in patients with various extragenital diseases, and can assist in developing methods for the selective transfer of the most promising embryos.
