US 12,333,715 B2
Method and system for selecting embryos
Jonathan Michael MacGillivray Hall, Adelaide (AU); Donato Perugini, Adelaide (AU); and Michelle Perugini, Adelaide (AU)
Assigned to ASTEC CO., Ltd., Fukuoka (JP)
Appl. No. 17/600,739
Filed by ASTEC CO., Ltd., Fukuoka (JP)
PCT Filed Apr. 2, 2020, PCT No. PCT/AU2020/000027
§ 371(c)(1), (2) Date Oct. 1, 2021,
PCT Pub. No. WO2020/198779, PCT Pub. Date Oct. 8, 2020.
Claims priority of application No. 2019901152 (AU), filed on Apr. 4, 2019.
Prior Publication US 2022/0198657 A1, Jun. 23, 2022
Int. Cl. G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06V 10/50 (2022.01); G06V 10/54 (2022.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01)
CPC G06T 7/0012 (2013.01) [G06T 7/11 (2017.01); G06V 10/50 (2022.01); G06V 10/54 (2022.01); G06V 10/82 (2022.01); G06V 20/695 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/30044 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method for computationally generating an Artificial Intelligence (AI) model configured to estimate an embryo viability score from an image, the method comprising:
receiving a plurality of images and associated metadata, wherein each image is captured during a pre-determined time window after In-Vitro Fertilization (IVF) and the pre-determined time window is 24 hours or less, and the metadata associated with the image comprises at least a pregnancy outcome label;
pre-processing each image comprising at least segmenting the image to identify a Zona Pellucida region;
generating an Artificial Intelligence (AI) model configured to generate an embryo viability score from an input image by:
training at least one Zona Deep Learning Model using a deep learning method, comprising training a deep learning model on a set of Zona Pellucida images in which the Zona Pellucida regions are identified, and the associated pregnancy outcome labels are at least used to assess the accuracy of a trained model; and
training one or more additional AI models wherein each additional AI model is either a computer vision model trained using a machine learning method that uses a combination of one or more computer vision descriptors extracted from an image to estimate an embryo viability score, a deep learning model trained on images localized to the embryo comprising both Zona Pellucida and IntraZonal Cavity (IZC) regions, and a deep learning model trained on a set of IntraZonal Cavity (IZC) images in which all regions apart from the IZC are masked, and either using an ensemble method to combine at least two of the at least one Zona deep learning model and the one or more additional AI models to generate the AI model embryo viability score from an input image or using a distillation method to train an AI model to generate the AI model embryo viability score using the at least one Zona deep learning model and the one or more additional AI models to generate the AI model; and
deploying the AI model.