CPC G16H 50/30 (2018.01) [A61B 5/4362 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); G06T 7/0012 (2013.01); G06T 11/60 (2013.01); G06V 10/82 (2022.01); G06V 20/698 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/10016 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30044 (2013.01); G06V 10/454 (2022.01)] | 20 Claims |
1. A computer-implemented method, including the steps of:
receiving video data of a human embryo, the video data captured by an image sensor of an incubator and including a sequence of images of the human embryo in chronological order, the video data including spatial dimensions and temporal dimensions;
applying at least one three-dimensional (3D) artificial neural network (ANN) including a 3D convolutional neural network (CNN) to the video data to determine a viability score for the human embryo, wherein the viability score represents a likelihood that the human embryo will result in a viable embryo or a viable fetus, the 3D convolutional neural network (CNN) being configured to extract features from both the spatial dimensions and the temporal dimensions of the video data by performing 3D convolutions, and the 3D CNN including a plurality of convolution layers each using a 3D convolution kernel, and a plurality of pooling layers each using a 3D pooling kernel that determine the viability score; and
outputting the viability score.
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