Abstract: The present disclosure encompasses systems and methods for predicting embryo ploidy. Specific embodiments encompass methods of non-invasively predicting ploidy status of an embryo, by receiving a dataset with a static image of the embryo, analyzing the static image by one or more machine and/or deep learning model via one or more classification task applied to the dataset; and generating an output prediction of the ploidy status of the embryo. Particular methods relate to methods wherein the dataset additionally includes one or more clinical and/or morphological features for the embryo. Embodiments also relate to predicting embryo viability and/or improving embryo selection, such as during in vitro fertilization, and uses thereof.

Abstract: The present disclosure encompasses systems and methods for predicting embryo ploidy. Specific embodiments encompass methods of non-invasively predicting ploidy status of an embryo, by receiving a dataset with video including a plurality of image frames of the embryo, analyzing the plurality of image frames by one or more machine and/or deep learning model via one or more classification task applied to the dataset; and generating an output prediction of the ploidy status of the embryo. Particular methods relate to methods wherein the dataset additionally includes one or more clinical and/or morphological features for the embryo, such as maternal age at the time of oocyte retrieval. Embodiments also relate to predicting embryo viability and/or improving embryo selection, such as during in vitro fertilization, and uses thereof.

Abstract: A method for classifying human blastocysts includes obtaining images of a set of artificially fertilized (AF) embryos incubating in an incubator. A morphological quality of the AF embryos is determined based on a classification of the images by a convolutional neural network trained using images of pre-classified embryos. Each of the AF embryos is graded based on the morphological quality. A probability that a given graded AF embryo will result in a successful pregnancy after the given AF embryo is implanted in a gestating female is computed for each of the AF embryos from the set based on a grade of the given AF embryo and clinical parameters associated with the gestating female. One or more graded AF embryos to be recommended to be implanted in the gestating female from the set are selected based on the probability of successful pregnancy.