Embryonic Quality Assessment using Advanced Deep Learning Architectures utilizing Microscopic Images of Blastocysts.

The accurate evaluation of embryonic quality which is a key aspect in Assisted Reproductive Technology (ART) and it is crucial to ensure the success of in vitro fertilization (IVF), especially at critical developmental phases like day 3 and day 5. To increase the success rate of implantation, morphological feature-based scoring techniques are currently the mainstay of embryo evaluation. Addressing the need for enhanced accuracy and latency, our work uses advanced algorithms based on deep learning to assess microscopic images of embryos at these critical junctures.