Abstract: The present invention extends to methods, systems, and computer program products for estimating oocyte quality. A machine learning algorithm accesses oocyte training data for a mammalian species (e.g., humans) and trains a neural network to estimate oocyte quality for the mammalian species based on the oocyte training data. The neural network accesses a microscopic image of an oocyte and identifies oocyte features of the oocyte. Based on the identified oocyte features, the neural network estimates oocyte quality, including: (a) predicting a probability of a corresponding embryo maintaining sufficient developmental competence until a specified time after fertilization and (b) predicting another probability of the corresponding embryo reaching a specific embryonic stage after fertilization. An oocyte is selected, from among a plurality of human oocytes including the human oocyte, for a potential recipient based at least in part on the oocyte quality, including based on the probability and the other probability.

Abstract: The present invention extends to methods, systems, and computer program products for semantically altering a medical image. A medical image and a transform are accessed. The transform is used to transform the medical image to a simpler image having reduced complexity relative to the medical image. A semantic alteration is made to content of the simpler image. Another (and possibly inverse) transform is accessed. The other transform is used to transform the simpler image to a more complex image having increased complexity relative to the simpler image (e.g., complexity resembling the medical image). Transforming the simpler image to a more complex image can include propagating the semantic alteration with the increased complexity into content of the more complex image. A medical decision is made in view of the semantic alteration and based on at least a portion of the more complex image content.

Abstract: The present invention extends to methods, systems, and computer program products for predicting embryo implantation probability. A neural network accesses a set of images depicting an embryo. The neural network determines a correlation between the set of images and images corresponding to other embryos considered during neural network training. The neural network derives an embryo implantation probability associated with the embryo based on known implantation outcomes associated with the other embryos and in view of clinical data associated with a potential recipient of the embryo. An embryo is selected for the potential recipient based at least in part on the derived embryo implantation probability. The neural network can also derive a confidence and/or explanation of why the neural network assigned an embryo implantation probability to an embryo. The confidence can be considered in embryo selection.