# US 2026/0212501 A1 · Method and system for evaluating blastocyst quality

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- Number: US 2026/0212501 A1
- Document ID: US20260212501A1
- Status: Published application
- Kind: A1
- Application number: 19/457,996
- Filed: 2026-01-23
- Published: 2026-07-23
- Earliest claimed: 2023-07-31
- Assignees: Suzhou Boundless Medical Technology Co., Ltd.
- Applicants: Suzhou Boundless Medical Technology Co., Ltd.
- Inventors: Chunfeng YUE; Hang LIU; Zongjie HUANG
- Web page: https://patentgrep.com/patent/US20260212501A1

## CPC

- G06T 7/0012: {Biomedical image inspection}
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/096: Transfer learning
- G06N 20/00: Machine learning
- G06T 3/40: Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06V 10/82: using neural networks
- G06V 20/693: {Acquisition}
- G06V 20/695: {Preprocessing, e.g. image segmentation}
- G06T 2207/10056: Microscopic image
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30044: Fetus; Embryo
- Y02P 90/30: Computing systems specially adapted for manufacturing
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS

## Abstract

The present invention relates to a method and system for evaluating blastocyst quality. The method includes: inputting a multi-focus image of a blastocyst into a pre-trained deep learning model, acquiring a development stage of the blastocyst, and acquiring quantitative scores of inner cell mass (ICM) and trophectoderm (TE); acquiring a quantitative score of blastocyst quality from the quantitative scores of ICM and TE; and selecting an optimum blastocyst according to the development stage of the blastocyst and the quantitative score of blastocyst quality. According to the present invention, the quality of blastocysts of the same grade is discriminated, and the overall quality of an embryo is further quantified.

## Claims

### Claim 1

A method for evaluating blastocyst quality, comprising:
inputting a multi-focus image of a blastocyst into a pre-trained deep learning model, acquiring a development stage of the blastocyst, and acquiring quantitative scores of inner cell mass (ICM) and trophectoderm (TE);
acquiring a quantitative score of blastocyst quality from the quantitative scores of ICM and TE; and
selecting an optimum blastocyst according to the development stage of the blastocyst and the quantitative score of blastocyst quality.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the pre-trained deep learning model comprises:
training a deep learning model with a pre-processed multi-focus image of the blastocyst, and acquiring the probabilities of ICM and TE grades.

### Claim 3 (depends on 2)

The method according to claim 2, wherein for the ICM and TE grades,
the ICM grades comprise: grade A, grade B, and grade C; and
the TE grades comprise: grade A, grade B, and grade C.

### Claim 4 (depends on 2)

The method according to claim 2, wherein the pre-processed multi-focus image of the blastocyst is obtained by a process comprising:
acquiring multiple photos at different focal planes that are set to have the same resolution; and
segmenting an embryo in each of the photos at different focal planes, filling the embryo to a resolution of a first set size and then scaling to a resolution of a second set size, wherein
the multiple photos at different focal planes at least comprise one photo of ICM and one photo of TE.

### Claim 5 (depends on 2)

The method according to claim 2, wherein the acquiring the probabilities of ICM and TE grades comprises:
extracting, by a backbone network in the deep learning model, a feature vector from each of blastocyst images at different focal planes;
acquiring, by inputting the feature vector into a fully connected network in the deep learning model, the development stage of the blastocyst and the ICM and TE grades; and
acquiring, by inputting the development stage of the blastocyst and the ICM and TE grades into the Softmax layer and normalizing, the probabilities of the ICM and TE grades.

### Claim 6 (depends on 3)

The method according to claim 3, wherein the acquiring quantitative scores of ICM and TE comprises:
acquiring, by weighted summation of the grade A, grade B, and grade C of ICM and the grade A, grade B, and grade C of TE respectively according to set weights, the quantitative scores of ICM and TE.

### Claim 7 (depends on 1)

The method according to claim 1, wherein the acquiring a quantitative score of blastocyst quality comprises:
acquiring weights of the quantitative scores of ICM and TE by a pre-trained logistic regression model; and
acquiring, by weighted summation of the quantitative scores of ICM and TE, the quantitative score of blastocyst quality.

### Claim 8 (depends on 1)

The method according to claim 1, wherein the pre-trained logistic regression model comprises:
acquiring, according to the quantitative scores of ICM and TE and an embryo clinical outcome, the weights of the quantitative scores of ICM and TE.

### Claim 9 (depends on 1)

The method according to claim 1, wherein the selecting an optimum blastocyst according to the development stage of the blastocyst and the quantitative score of blastocyst quality comprises:
selecting a blastocyst with a development stage of the blastocyst that is greater than or equal to phase III; and
based on the selected blastocyst with a development stage of the blastocyst that is greater than or equal to phase III, selecting a blastocyst with the highest quantitative score of blastocyst quality.

### Claim 10 (depends on 1)

A system for evaluating blastocyst quality, for implementing the method for evaluating blastocyst quality according to claim 1, comprising:
an imaging device: configured to take a multi-focus image of a blastocyst;
a scoring device: configured to determine a development stage of the blastocyst and scores of ICM and TE; and
a selecting device: configured to select a blastocyst with the highest blastocyst quality score.