0000000000400990
AUTHOR
Nikica Zaninovic
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization
AbstractVisual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality with…
Assessment of embryo morphology and developmental dynamics by time-lapse microscopy: is there a relation to implantation and ploidy?
Time-lapse microscopy (TLM) is an exciting novel technology with great potential for enhancing embryo selection in the embryology laboratory. This non-invasive objective assessment of embryos has provided a new tool for predicting embryo development and implantation potential. TLM detects several morphological phenomena that are often missed with static observations using conventional incubators, such as irregular divisions, blastocyst collapse and re-expansion, timing of blastocoel appearance, and timing of formation and internalization of fragments. Nevertheless, it should be recognized that conventional morphological assessment has been widely accepted as the gold standard by most embryo…
Robust Automated Assessment of Human Blastocyst Quality using Deep Learning
AbstractMorphology assessment has become the standard method for evaluation of embryo quality and selecting human blastocysts for transfer inin vitro fertilization(IVF). This process is highly subjective for some embryos and thus prone to human bias. As a result, morphological assessment results may vary extensively between embryologists and in some cases may fail to accurately predict embryo implantation and live birth potential. Here we postulated that an artificial intelligence (AI) approach trained on thousands of embryos can reliably predict embryo quality without human intervention.To test this hypothesis, we implemented an AI approach based on deep neural networks (DNNs). Our approac…