System and method for selecting artificially fertilized embryos

A system for classifying human blastocysts, comprising;

• an input module configured to receive images of a set of artificially fertilized (AF) embryos incubating in an incubator;
• a morphological quality evaluation module configured to determine a morphological quality of the AF embryos based on a classification of the images by a deep neural network trained using images of pre-classified embryos, wherein the deep neural network is trained using the pre-classified embryos and clinical data associated with the pre-classified embryos, the clinical data associated with the pre-classified embryos comprising an age of a donor female providing eggs that were fertilized to obtain the pre-classified embryos and a presence of clinical conditions associated with the donor female, the clinical conditions including diabetes and obesity;
• a pregnancy success computing module configured to compute a probability that a given AF embryo will result in a successful pregnancy after the given AF embryo is implanted in a gestating female for each of the AF embryos from the set based on the morphological quality of the AF embryos and clinical parameters associated with the gestating female;
• and
• an embryo selection module configured to select one or more of the AF embryos from the set based on the probability of successful pregnancy, and to output an identity of each of the selected AF embryos and a number of the selected AF embryos to be recommended to be implanted in the gestating female based on a desired outcome input into the embryo selection module following implantation.

A non-transitory computer readable medium storing a computer readable program of classifying human blastocysts, comprising;

• computer readable instructions to determine a morphological quality of artificially fertilized (AF) embryos based on a classification of images of the AF embryos by a convolutional neural network trained using images of pre-classified embryos, wherein the convolutional neural network is trained using the pre-classified embryos and clinical data associated with the pre-classified embryos, the clinical data associated with the pre-classified embryos comprising an age of a donor female providing eggs that were fertilized to obtain the pre-classified embryos and a presence of clinical conditions associated with the donor female, the clinical conditions including diabetes and obesity;
• computer readable instructions to compute a probability that a given AF embryo will result in a successful pregnancy after the given AF embryo is implanted in a gestating female for each of the AF embryos, the probability being computed based on the morphological quality of the given AF embryo and clinical parameters associated with the gestating female;
• and
• computer readable instructions to select one or more of the AF embryos based on the morphological quality of the AF embryos and clinical parameters associated with the gestating female, and provide an identity and number of AF embryos that will provide the highest likelihood of live birth following implantation in the gestating female.