SUMMARY

Combining state-of-the-art artificial intelligence (AI), machine learning (ML), video image data processing and optics systems, practical insights from clinical embryologists and scientific input from data engineers, the present technology provides the world's most comprehensive embryo evaluation system for detecting and/or predicting embryo morphokinetics, viability and developmental potential in a single, easy to use system. The technology processes digital video image data from real-time video taken of an embryo proximate in time before transfer or storage such as by cryopreservation. The image data of the resulting signal is then processed to reveal information, heretofore hidden, to the operator and other interested parties. With this technology, model-processed real-time video image data of an embryo can predict, among other things: (1) the embryo's present viability at time of transfer, (2) the embryo's inviability at time of planned transfer, (3) the embryo's likelihood (qualitatively, quantitative and/or probabilistic) to result in pregnancy and produce live offspring, (4) the embryo's likelihood of producing a male offspring, (5) the embryo's likelihood of producing a female offspring, (6) genetic inferiority (likelihood to embody disease or genetic issue, and of what type) of the embryo and (7) genetic superiority (likelihood to produce desired traits in offspring) of the embryo. The instant deep learning model(s) can also rank a number (group) of embryos based on one or more predictive embryo characteristics or predicted offspring traits based on generated relative strengths (probability of occurrence) of the predicted characteristic among the group of blastocysts enabling an embryologist's selection and transfer of the most desirable embryos into recipient(s).

In one embodiment, video image data is utilized to develop deep learning models that provide the basis for an AI/ML-based software package that enables a non-invasive, quantitative, non-subjective system and method to identify high-quality, viable embryos likely to result in pregnancy and healthy offspring as exemplified inFIG. 1of the accompanying drawings. Currently, embryologists have limited ability to evaluate embryo health and viability, making it all too common to unknowingly transfer dead or otherwise inviable embryos into recipients. The present technology allows a user to make more informed decisions about which embryo(s) to transfer and reduces the transfer of low-quality embryos which will not establish pregnancy. Technologies such as this that improve pregnancy outcomes of IVF beneficially affect patients both physically and psychologically, as well as having a positive financial impact on the industry as a whole through cost reduction. Another aim of this technology is to enhance the workflow of the user by enabling a single multi-embryo video/scan to provide immediate feedback through real time image data collection and processing. In the case of animal breeding and production, the technology is utilized to select the highest quality embryos that are most likely to achieve pregnancy and produce disease-free offspring that embody the most superior of desired traits, including sex.

