Abstract: Methods and systems are for improvements to in-vitro fertilization using morpho-kinetic signatures. These improvements are achieved by analyzing a series of images of a developing embryo (e.g., time-lapse images) as opposed to a single static image. For example, due to the difficulty in identifying clear distinctions between morphological states based on static images, as well as the unpredictability of morpho-kinetic development of an embryo, the system analyzes the development of an embryo as a whole over a given time frame (e.g., fertilization to blastulation), which provides a better prediction of the viability of a given embryo. The analysis may take the form of a morpho-kinetic signature, which itself may be used to determine an optimal time to transfer and/or implant an embryo into a patient.

Abstract: Methods and systems are described for improvements in embryo selection. These improvements are achieved by analyzing a series of images of a developing embryo (e.g., time-lapse images) as opposed to a single static image. For example, due to the difficulty in identifying clear distinctions between morphological states based on static images as well as the unpredictability of morpho-kinetic development of an embryo, the system analyzes the development of an embryo as a whole over a given time frame (e.g., fertilization to blastulation), which provides a better prediction of the viability of a given embryo. The analysis may take the form of a morpho-kinetic signature, which itself may be used to classifying embryos.

Abstract: Methods and systems are described for predicting probabilities of unfertilized eggs becoming blastocysts. For example, a method can include a trained machine-learning model receiving images of unfertilized eggs. The trained machine-learning model can determine predictions for each of the unfertilized eggs becoming blastocysts based at least on the appearance of the unfertilized eggs in the images. The predictions can be provided by the trained machine-learning model. Based on the predictions, a distribution of the unfertilized eggs into cohorts can be determined, including. Then, based on the cohorts, a second prediction of a cohort producing at least one blastocysts can be determined.

Abstract: Methods and systems are described for improvements in embryo selection. These improvements are achieved by analyzing a series of images of a developing embryo (e.g., time-lapse images) as opposed to a single static image. For example, due to the difficulty in identifying clear distinctions between morphological states based on static images as well as the unpredictability of morpho-kinetic development of an embryo, the system analyzes the development of an embryo as a whole over a given time frame (e.g., fertilization to blastulation), which provides a better prediction of the viability of a given embryo. The analysis may take the form of a morpho-kinetic signature, which itself may be used for classifying embryos.

Abstract: Methods and systems are described for classifying unfertilized eggs. For example, using control circuitry, first images of fertilized eggs can be received, and the first images can be labeled with known classifications. Using the control circuitry, an artificial neural network can be trained to detect the known classifications based on the first images of the fertilized eggs and a second image can be received of an unfertilized egg with an unknown classification. Using the control circuitry, the second image can be input into the trained artificial neural network and a prediction from the trained artificial neural network can be received that the second image corresponds to one or more of the known classifications.

Abstract: Methods and systems are disclosed for improvements to determining the implantation potential of embryos. These improvements are achieved by determining a predicted implantation potential of an embryo. Calculating the predicted implantation potential can include receiving a first feature input based on a morphokinetic signature of an embryo. A first feature output can be determined based on a classification of the embryo as euploid or aneuploid. The first feature output may be input into a second artificial neural network to generate a second feature output based on a predicted implantation potential of the embryo. The second artificial neural network may be trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures and known implantation data. A recommendation for implantation based on the second feature output may be generated for display at a user interface.

Abstract: Methods and systems are described for improvements in embryo selection. These improvements are achieved by analyzing a series of images of a developing embryo (e.g., time-lapse images) as opposed to a single static image. For example, due to the difficulty in identifying clear distinctions between morphological states based on static images as well as the unpredictability of morpho-kinetic development of an embryo, the system analyzes the development of an embryo as a whole over a given time frame (e.g., fertilization to blastulation), which provides a better prediction of the viability of a given embryo. The analysis may take the form of a morpho-kinetic signature, which itself may be used for classifying embryos.

Abstract: Methods and systems are for improvements to in-vitro fertilization using morpho-kinetic signatures. These improvements are achieved by analyzing a series of images of a developing embryo (e.g., time-lapse images) as opposed to a single static image. For example, due to the difficulty in identifying clear distinctions between morphological states based on static images, as well as the unpredictability of morpho-kinetic development of an embryo, the system analyzes the development of an embryo as a whole over a given time frame (e.g., fertilization to blastulation), which provides a better prediction of the viability of a given embryo. The analysis may take the form of a morpho-kinetic signature, which itself may be used to determine an optimal time to transfer and/or implant an embryo into a patient.