In-vitrofertilisatie (IVF)

Accurate evaluation of embryo viability is crucial for optimizing IVF treatments and increasing pregnancy rates. Usually, embryologists depend on non-invasive visual evaluations of embryos or oocytes. This assessment emphasizes characteristics like morphology and dynamic development, particularly during the blastocyst stage. However, this evaluation is inherently subjective, leading to variability between different observers and even within the same observer, influenced by factors such as embryo scoring systems and the embryologists experience and expertise, ultimately affecting the final success rate. Moreover, the transfer of multiple embryos per cycle, while potentially increasing the success rate, raises the risk of complications like pre-eclampsia and maternal haemorrhage, as well as the likelihood of multiple pregnancies.

The integration of AI with automated morphological analysis of embryos or blastocysts presents an enticing solution. In 2012, Santos Filho and colleagues presented a semi-automated grading technique. This method involved tasks such as image segmentation and classification specifically applied to human blastocyst images. The researchers employed SVM classifiers to evaluate the inner cell mass (ICM) and trophectoderm (TE) quality. Through an analysis of fractal dimensions and mean thickness in TE and ICM image textures, they successfully characterized the crucial morphological attributes of the blastocyst. Additionally, optimizing microscope settings, such as enhancing contrast and delineating individual features more distinctly, may further refine image analysis.

Adapted from: JM Raimundo, P Cabrita, "Artificial intelligence at assisted reproductive technology," Procedia Computer Science, Volume 181, 2021, Pages 442-447, ISSN 1877-0509, DOI: 10.1016/j.procs.2021.01.189. Full Article.