Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
38083389
Selecting the single best blastocyst based on morphological appearance for implantation is a crucial part of in vitro fertilization (IVF). Various deep learning and computer vision-based methods have recently been applied for assessing blastocyst qua...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
38082658
The success rate of bovine in vitro embryo reproduction is low and highly dependent on the oocyte quality. The selection of the oocyte to be fertilized is done by the embryologists' visual examination of oocytes. It is time-consuming, subjective, and...
RESEARCH QUESTION: What is the effect of increasing training data on the performance of ongoing pregnancy prediction after single vitrified-warmed blastocyst transfer (SVBT) in a deep-learning model?
In recent years, increasing efforts have been made to develop advanced techniques that could predict the potential of implantation of each single embryo and prioritize the transfer of those at higher chance. The most promising include non-invasive pr...
RESEARCH QUESTION: Can a deep learning image analysis model be developed to assess oocyte quality by predicting blastocyst development from images of denuded mature oocytes?
BACKGROUND: Genetic mosaicism is commonly observed in human blastocysts. Embryos' morphokinetic feature observed from time-lapse monitoring (TLM) is helpful to predict the embryos' ploidy status in a non-invasive way. However, morphokinetic research ...
BACKGROUND: Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to...
STUDY QUESTION: Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF?