Embryo quality evaluation method, device and equipment and computer readable storage medium

Abstract

The invention provides an embryo quality evaluation method, device and equipment and a computer readable storage medium, and relates to the technical field of medical treatment. The method comprises the following steps: acquiring a three-dimensional reconstruction model of a to-be-evaluated embryo in a preset cultivation stage; performing feature extraction on the three-dimensional reconstruction model to obtain spatial mutual features of trophoblast cells and inner cell clusters in the embryo to be evaluated; and according to the spatial mutual characteristics, performing quality evaluation on the to-be-evaluated embryo by adopting a preset embryo quality evaluation model to obtain a predicted value of a quality evaluation parameter of the to-be-evaluated embryo. According to the method, spatial mutual characteristics are excavated, and quality evaluation is carried out, so that the embryo with the most developmental potential can be more accurately selected clinically.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to an embryo quality assessment method, device, equipment and computer-readable storage medium. Background Art

[0002] With the rapid development of computer vision and assisted reproductive technology, the application of artificial intelligence in the field of assisted reproduction is becoming increasingly in-depth. How to simulate the spatial structure of the blastocyst through three-dimensional reconstruction technology without damaging the embryo, thereby revealing information that cannot be observed from a two-dimensional perspective, has always been one of the focuses of embryology experts.

[0003] Since its introduction into the field of embryo culture, time-difference culture technology has brought revolutionary changes to assisted reproductive technology, overturned the traditional working mode of embryologists, and greatly improved work efficiency. With the help of time-difference culture technology, not only can more embryo characteristics be obtained, but it also facilitates the formation of a variety of embryo scoring systems. In addition, the mature application of time-difference culture technology in the field of assisted reproduction has laid a solid foundation for non-invasive three-dimensional reconstruction of embryos. Through the embryo three-dimensional reconstruction technology based on time-difference culture imaging data, the three-dimensional spatial structure of the blastocyst can be accurately simulated, which provides a new way to deeply explore the characteristics of the blastocyst period and lays a data foundation for building a quality assessment model.

[0004] Shan, G. (2022) applied the 3D reconstruction technology of blastocyst surface based on surface reconstruction to extract some 3D trophoblast information, including the number of trophoblast cells (TE), cell density, cell size, and the size of inner cell mass (ICM) (2D features), and created a scoring system for predicting PGT results. The 3D reconstruction method proposed by Shan, G. (2022) can be briefly summarized as unfolding a 3D spherical structure. The specific implementation scheme is: fix the embryo with two needles, then rotate it at a fixed angle each time, match the pixel position of the acquired image, and segment the trophoblast and inner cell mass, so as to extract the features of the inner cell mass and trophoblast cells, and construct a PGT prediction model based on the extracted features. This 3D reconstruction method is more inclined to obtain more complete trophoblast cell features, but it does not further study and measure the relationship between the two important subjects of the inner cell mass and trophoblast cells in the three-dimensional spatial structure. Summary of the invention

[0005] The purpose of the present invention is to provide an embryo quality assessment method, device, equipment and computer-readable storage medium to address the deficiencies in the above-mentioned prior art, so as to help clinicians more accurately select embryos with the greatest developmental potential.

[0006] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0007] In a first aspect, an embodiment of the present application provides an embryo quality assessment method, comprising obtaining a three-dimensional reconstructed model of the embryo to be assessed at a preset cultivation stage; performing feature extraction on the three-dimensional reconstructed model to obtain spatial mutual characteristics of trophoblast cells and inner cell mass in the embryo to be assessed; and performing quality assessment on the embryo to be assessed using a preset embryo quality assessment model based on the spatial mutual characteristics to obtain predicted values ​​of quality assessment parameters of the embryo to be assessed.

[0008] In one embodiment, the spatial mutual characteristics include: spatial mutual characteristics of at least two dimensions; based on the spatial mutual characteristics, a preset embryo quality assessment model is used to perform quality assessment on the embryo to be assessed, and a predicted value of the quality assessment parameter of the embryo to be assessed is obtained, including: based on the spatial mutual characteristics of at least two dimensions, a preset embryo quality assessment model is used to perform quality assessment on the embryo to be assessed, and a predicted value of the quality assessment parameter of the embryo to be assessed is obtained.

[0009] In one embodiment, before performing quality assessment on the embryo to be assessed based on the spatial mutual characteristics of at least two dimensions using a preset embryo quality assessment model to obtain predicted values ​​of quality assessment parameters of the embryo to be assessed, the method further includes: performing feature analysis on clinical embryo data to obtain a correlation between the spatial mutual characteristics of at least two dimensions and preset clinical quality assessment parameters; constructing sample data based on the correlation; and performing model training using the sample data to obtain the embryo quality assessment model.

[0010] In one embodiment, the preset clinical quality assessment parameters include: at least two preset clinical quality assessment parameters of true clinical live birth rate, true clinical miscarriage rate, and true clinical pregnancy rate; the feature analysis of the clinical embryo data to obtain spatial mutual characteristics of at least two dimensions and a correlation relationship between the preset clinical quality assessment parameters includes: performing feature analysis on the clinical embryo data to obtain spatial mutual characteristics of at least two dimensions and a set of said correlation relationships between the at least two preset clinical quality assessment parameters.

[0011] In one embodiment, the spatial mutual characteristics of at least two dimensions include: the distance between the inner cell mass and the trophoblast cells, the contact area between the inner cell mass and the trophoblast cells, and the contact ratio between the inner cell mass and the trophoblast cells; wherein the distance is used to characterize the degree of squeezing between the inner cell mass and the trophoblast cells; and the contact ratio is: the ratio of the contact area to the surface area of ​​the inner cell mass of the three-dimensional reconstructed model.

[0012] In one embodiment, the feature analysis of the clinical embryo data to obtain the correlation between spatial mutual characteristics of at least two dimensions and preset clinical quality assessment parameters includes: feature classification of the clinical embryo data to obtain a clinical spatial mutual characteristic data set; feature analysis of the clinical spatial mutual characteristic data set to obtain the correlation between spatial mutual characteristics of at least two dimensions and the preset clinical quality assessment parameters.

[0013] In a second aspect, an embodiment of the present application also provides an embryo quality assessment device, comprising: an acquisition module, configured to acquire a three-dimensional reconstructed model of the embryo to be assessed at a preset cultivation stage; a feature extraction module, configured to perform feature extraction on the three-dimensional reconstructed model to obtain spatial mutual characteristics of trophoblast cells and inner cell mass in the embryo to be assessed; a prediction module, configured to perform quality assessment on the embryo to be assessed based on the spatial mutual characteristics using a preset embryo quality assessment model to obtain a predicted value of a quality assessment parameter of the embryo to be assessed.

[0014] In one embodiment, the spatial mutual characteristics include: spatial mutual characteristics of at least two dimensions; the prediction module is configured to: based on the spatial mutual characteristics of at least two dimensions, use a preset embryo quality assessment model to perform quality assessment on the embryo to be assessed, and obtain a predicted value of the quality assessment parameter of the embryo to be assessed.

[0015] In one embodiment, the embryo quality assessment device also includes a model training module, which is configured to: perform feature analysis on clinical embryo data to obtain the correlation between spatial mutual features of at least two dimensions and preset clinical quality assessment parameters; construct sample data based on the correlation; use the sample data to perform model training to obtain the embryo quality assessment model.

[0016] In one embodiment, the preset clinical quality assessment parameters include: at least two preset clinical quality assessment parameters of true clinical live birth rate, true clinical miscarriage rate, and true clinical pregnancy rate; the model training module is configured to: perform feature analysis on the clinical embryo data to obtain spatial mutual features of at least two dimensions and a set of correlation relationships between the at least two preset clinical quality assessment parameters.

[0017] In one embodiment, the spatial mutual characteristics of at least two dimensions include: the distance between the inner cell mass and the trophoblast cells, the contact area between the inner cell mass and the trophoblast cells, and the contact ratio between the inner cell mass and the trophoblast cells; wherein the distance is used to characterize the degree of squeezing between the inner cell mass and the trophoblast cells; and the contact ratio is: the ratio of the contact area to the surface area of ​​the inner cell mass of the three-dimensional reconstructed model.

[0018] In one embodiment, the model training module is configured to: perform feature classification on the clinical embryo data to obtain a clinical spatial mutual feature data set; perform feature analysis on the clinical spatial mutual feature data set to obtain a correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters.

[0019] In a third aspect, an embodiment of the present application further provides a computer device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores program instructions executable by the processor, and when the computer device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of any one of the above methods.

[0020] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the above methods are executed.

[0021] The beneficial effects of the present application are: obtaining a three-dimensional reconstruction model of the embryo to be evaluated at a preset cultivation stage; extracting features from the three-dimensional reconstruction model to obtain the spatial mutual features of the trophoblast cells and the inner cell mass in the embryo to be evaluated; and using a preset embryo quality assessment model to perform quality assessment on the embryo to be evaluated based on the spatial mutual features to obtain the predicted values ​​of the quality assessment parameters of the embryo to be evaluated. By mining the spatial mutual features and performing quality assessment, it is possible to help clinicians more accurately select the embryos with the greatest developmental potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of a process for evaluating embryo quality provided in an embodiment of the present application;

[0024] Figure 2 A bottom view of the relative positions of trophoblast cells and inner cell mass on a three-dimensional reconstructed model in an embryo quality assessment method provided in an embodiment of the present application;

[0025] Figure 3 A left view of the relative positions of trophoblast cells and inner cell mass on a three-dimensional reconstructed model in a method for evaluating embryo quality provided in an embodiment of the present application;

[0026] like Figure 4 A front view of the relative positions of trophoblast cells and inner cell mass on a three-dimensional reconstructed model in a method for evaluating embryo quality provided in an embodiment of the present application;

[0027] Figure 5 A top view of the relative positions of trophoblast cells and inner cell mass on a three-dimensional reconstructed model in a method for evaluating embryo quality provided in an embodiment of the present application;

[0028] Figure 6 A right view of the relative positions of trophoblast cells and inner cell mass on a three-dimensional reconstructed model in a method for evaluating embryo quality provided in an embodiment of the present application;

[0029] Figure 7 A rear view of the relative positions of trophoblast cells and inner cell mass on a three-dimensional reconstructed model in a method for evaluating embryo quality provided in an embodiment of the present application;

[0030] Figure 8 A schematic diagram of a process for evaluating embryo quality provided in an embodiment of the present application;

[0031] Fig. 9 A schematic diagram of a process for evaluating embryo quality provided in an embodiment of the present application;

[0032] Fig.10 A schematic diagram of the structure of an embryo quality assessment device provided in an embodiment of the present application;

[0033] Fig.11 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0035] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0036] In the description of the present application, it should be noted that if the terms "upper", "lower", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, or is the orientation or position relationship in which the product of the application is usually placed when used. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0037] In addition, the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] It should be noted that, in the absence of conflict, the features in the embodiments of the present application may be combined with each other.

[0039] Figure 1 A schematic diagram of a method for evaluating embryo quality provided in an embodiment of the present application; Figure 1 As shown, the method includes:

[0040] Step 110: Obtain a three-dimensional reconstructed model of the embryo to be evaluated at a preset culture stage.

[0041] The three-dimensional reconstruction model can be obtained by performing three-dimensional spatial reconstruction on the blastocyst image data through three-dimensional spatial reconstruction technology.

[0042] 3D space reconstruction technology is a technology that uses a camera to scan or photograph an object or scene and uses a computer algorithm to convert the 2D photo data into an interactive 3D model. This technology mainly relies on deep learning methods, using the powerful learning and fitting capabilities of deep neural networks to process RGB or RGBD images to restore the 3D model of the object.

[0043] The preset culture stage can be the blastocyst stage. In actual operation, blastocyst image data with 11 or more focal planes under the time-lapse culture system (Hoffman imaging) can be selected for three-dimensional spatial reconstruction. It should be noted that when selecting blastocyst image data, the inner cell mass must be within the field of view, the number of focal planes where the inner cell mass is located must be greater than 3, and there must be fewer fragments (too many fragments will affect the accuracy of the three-dimensional reconstruction model).

[0044] Step 120: extract features from the three-dimensional reconstructed model to obtain spatial mutual features of trophoblast cells and inner cell mass in the embryo to be evaluated.

[0045] Among them, the spatial mutual characteristics of trophoblast cells and inner cell mass are characteristics defined and proposed by an embryo quality assessment method provided in an embodiment of the present application, which are used to represent the spatial relationship characteristics between the inner cell mass and the trophoblast cells; for example: the spatial distance between the inner cell mass and the trophoblast cells, the spatial contact area between the inner cell mass and the trophoblast cells, the spatial contact ratio between the inner cell mass and the trophoblast cells, etc.; wherein the distance and the spatial contact ratio are both used to characterize the degree of squeezing of the inner cell mass and the trophoblast cells; the contact ratio is: the ratio of the contact area to the surface area of ​​the inner cell mass of the three-dimensional reconstructed model.

[0046] This step can also extract the characteristics of trophoblast cells and inner cell mass. The characteristics of trophoblast cells and inner cell mass are consistent with the characteristics extracted in related technologies, such as: the number of trophoblast cells, the density of trophoblast cells, the surface area of ​​the inner cell mass, the volume of the inner cell mass, etc.

[0047] Step 130: Based on the spatial mutual characteristics, a preset embryo quality assessment model is used to perform quality assessment on the embryo to be assessed, and a predicted value of a quality assessment parameter of the embryo to be assessed is obtained.

[0048] Among them, quality assessment models include decision tree prediction models, logistic regression prediction models, etc.

[0049] In actual operation, the Gardner scoring system can be used as a performance baseline, and the spatial mutual features defined and proposed by an embryo quality assessment method provided in an embodiment of the present application can be added to the Gardner scoring system to establish a decision tree prediction model for performance comparison.

[0050] The Gardner scoring system is a method used to evaluate the potential of embryonic development. It is mainly based on the expansion state of the blastocyst, the development of the inner cell mass and the trophectoderm. This system was proposed by David Gardner and is currently one of the most commonly used blastocyst scoring methods. Specifically, the Gardner scoring system includes the following aspects:

[0051] 1. Expansion degree of blastocyst cavity: According to the size of blastocyst cavity and whether it is hatched, the development of blastocyst is divided into six periods, from early chambered blastocyst to hatched blastocyst.

[0052] 2. Grading of inner cell mass: According to the number of cells and the density of arrangement, the inner cell mass is divided into three grades: A, B, and C. Grade A indicates a large number of cells with dense arrangement, while grade C indicates a small number of cells.

[0053] 3. Grading of trophoblast cells: Trophoblast cells are divided into three grades: A, B, and C, also based on the number of cells and the density of arrangement.

[0054] The Gardner scoring system is widely used and is mainly used to evaluate the quality and developmental potential of embryos. Blastocysts with higher scores (such as 4AA, 4BB, etc.) are considered high-quality embryos with a higher transplant success rate. In addition, the developmental stage of the blastocyst, the grading of the inner cell mass and trophoblast cells are also used to determine whether the embryo is suitable for transplantation or cryopreservation.

[0055] Through the Gardner scoring system, doctors can more accurately assess the quality of embryos, thereby improving the success rate of IVF. At the same time, the system also provides patients with more choices and hope, making assisted reproductive technology more scientific and effective.

[0056] The embryo quality assessment method provided in the embodiment of the present application first obtains a three-dimensional reconstruction model of the embryo to be assessed at a preset cultivation stage; secondly, extracts features from the three-dimensional reconstruction model to obtain the spatial mutual features of the trophoblast cells and the inner cell mass in the embryo to be assessed; finally, based on the spatial mutual features, a preset embryo quality assessment model is used to perform quality assessment on the embryo to be assessed, and the predicted value of the quality assessment parameter of the embryo to be assessed is obtained. By mining the spatial mutual features and performing quality assessment, it can help the clinician to more accurately select the embryo with the greatest developmental potential.

[0057] The following is a further explanation of the process of using a preset embryo quality assessment model to perform quality assessment on the embryo to be assessed and obtaining a predicted value of the quality assessment parameter of the embryo to be assessed in the above embodiment in combination with an example. The spatial mutual characteristics include: spatial mutual characteristics of at least two dimensions; the above step 130 may further include the following steps:

[0058] According to the spatial mutual characteristics of at least two dimensions, a preset embryo quality assessment model is used to perform quality assessment on the embryo to be assessed, and a predicted value of the quality assessment parameter of the embryo to be assessed is obtained.

[0059] Among them, the spatial mutual characteristics of at least two dimensions include: the distance between the inner cell mass and the trophoblast cells, the contact area between the inner cell mass and the trophoblast cells, and the contact ratio between the inner cell mass and the trophoblast cells;

[0060] The distance is used to characterize the degree of squeezing between the inner cell mass and the trophoblast cells; the contact ratio is the ratio of the contact area to the surface area of ​​the inner cell mass of the three-dimensional reconstructed model.

[0061] like Figure 2As shown, it is a bottom view of the relative positions of trophoblast cells and inner cell mass on the three-dimensional reconstruction model; Figure 3 As shown, this is a left view of the relative positions of trophoblast cells and inner cell mass on the three-dimensional reconstruction model; Figure 4 As shown, it is a front view of the relative positions of trophoblast cells and inner cell mass on the three-dimensional reconstruction model; Figure 5 As shown, it is a top view of the relative positions of trophoblast cells and inner cell mass on the three-dimensional reconstruction model; Figure 6 As shown, this is the right view of the relative positions of trophoblast cells and inner cell mass on the three-dimensional reconstruction model; Figure 7 Shown is a posterior view of the relative positions of trophoblast cells and inner cell mass on the three-dimensional reconstructed model.

[0062] The embryo quality assessment method provided in the embodiment of the present application uses a preset embryo quality assessment model to perform quality assessment on the embryo to be assessed according to the spatial mutual characteristics of at least two dimensions, and obtains the predicted value of the quality assessment parameter of the embryo to be assessed. In this way, by adding the spatial mutual characteristics of at least two dimensions, the embryo quality assessment model can perform quality assessment more accurately.

[0063] The following is a continued explanation of the process of using an embryo quality assessment model to conduct quality assessment on an embryo to be assessed based on spatial mutual features of at least two dimensions and obtaining predicted values ​​of quality assessment parameters of the embryo to be assessed in the above embodiment in combination with examples. Figure 8 This is a schematic diagram of the process of the embryo quality assessment method provided in the embodiment of the present application. Figure 8 As shown, before the above step 130, the following steps 810 to 830 may be further included:

[0064] Step 810: Perform feature analysis on clinical embryo data to obtain correlations between spatial mutual features in at least two dimensions and preset clinical quality assessment parameters.

[0065] Among them, the preset clinical quality assessment parameters include true clinical pregnancy rate, true clinical live birth rate, true clinical miscarriage rate, etc.

[0066] Feature analysis can be understood as the correlation analysis between features and preset clinical quality assessment parameters. Specifically, it is to analyze the correlation between spatial mutual features of at least two dimensions and preset clinical quality assessment parameters.

[0067] In actual operation, the Mannwhitney variance can be used for continuous data analysis, and the chi-square test can be used for discrete data analysis. Generally, the number of trophoblast cells in the first three-dimensional quadrant where the inner cell mass is located, the proportion of the number of trophoblast cells in the first three-dimensional quadrant, the total number of trophoblast cells, the density of trophoblast cells; the volume of the inner cell mass, the surface area of ​​the inner cell mass are continuous data, for example: the continuous data are discretized by equal width discretization, equal frequency discretization, quantile discretization, cluster discretization, custom discretization, discretization based on statistical parameters, discretization based on decision trees, decision tree binning and other discretization methods.

[0068] The feature analysis results are as follows:

[0069] 1) The spatial contact area between the inner cell mass and the trophoblast cells, and the spatial contact ratio between the inner cell mass and the trophoblast cells: In live embryos, the contact area between ICM and TE is larger. Specifically, when the contact area is greater than 1196.4um2, the clinical live birth rate of blastocysts is higher and the miscarriage rate is lower (p<0.01 and p<0.05); when the contact area between ICM and TE accounts for 1.34%-2.7% of the surface area of ​​ICM, the blastocyst has the highest clinical pregnancy rate (p<0.01); from a biological point of view, a larger contact area can provide more opportunities for the exchange of nutrients between ICM and TE; at the same time, the contact surface is also an important area for signal transduction between ICM and TE. Through the signal transmission in this area, ICM and TE can coordinate each other's development and differentiation; in addition, the contact surface is also an indicator of the stability of blastocyst results, which directly affects the further development and implantation in the uterus;

[0070] 2) Spatial distance between inner cell mass and trophoblast cells: Compared with embryos without pregnancy and non-live birth, the spatial distance between ICM and TE in pregnant and live birth embryos is larger; specifically, when the spatial distance between ICM and TE is greater than 0.56, the live birth rate of blastocysts is the highest (p < 0.05) and the miscarriage rate is the lowest (p < 0.001).

[0071] The characteristics of trophoblast cells include: the total number of trophoblast cells, the density of trophoblast cells; the characteristics of inner cell mass include: the volume of inner cell mass, the surface area of ​​inner cell mass.

[0072] Among them, the characteristics of trophoblast cells and the characteristics of inner cell mass are the characteristics that are extracted in the related technology, and therefore, can be regarded as basic characteristics. Here, the density of trophoblast cells = the total number of trophoblast cells / the surface area of ​​blastocyst*1000.

[0073] Furthermore, before performing feature analysis, the data can be cleaned and then subjected to correlation analysis. If there is a linear relationship between the data, feature analysis will not be performed on the data; if there is a nonlinear relationship between the data, feature analysis will be performed on the data. Specifically, in the data cleaning stage, abnormal data and data that are not the above-mentioned feature data can be deleted; in the correlation analysis stage, a scatter plot between features can be drawn to determine whether there is a linear relationship between the feature data. Among them, correlation analysis is mainly used to analyze the difference between new features and original features. For example, the number of trophoblast cells in the first three-dimensional quadrant where the inner cell mass is located and the proportion of the number of trophoblast cells in the first three-dimensional quadrant can be regarded as new features, and the number of trophoblast cells and the density of trophoblast cells can be regarded as original features.

[0074] Step 820: Construct sample data according to the association relationship.

[0075] Among them, the characteristic data in the association relationship can be used as the characteristic sample, the preset clinical quality assessment parameters in the association relationship can be used as the labeled sample of the above-mentioned characteristic sample, and the characteristic sample and the labeled sample can be used as sample data.

[0076] When the preset clinical quality assessment parameter is one, the association relationship is one group, and each feature sample has one labeled sample; when the preset clinical quality assessment parameters are two, the association relationship is two groups, and each feature sample has two labeled samples, and so on.

[0077] Step 830: Use sample data to perform model training to obtain an embryo quality assessment model.

[0078] Among them, all data can be divided into training data and test data in chronological order. For example, the data from 2018 to 2020 are used as training data, and the data from 2021 are used as test data. The training data is first used to train the embryo quality assessment model to be trained, and then the test data is used to test the embryo quality assessment model to determine its evaluation effect.

[0079] In practice, the performance of the embryo quality assessment model has been improved by adding correlations (correlations between at least two dimensions of spatial mutual features and preset clinical outcomes); the specific model performance is as follows:

[0080] 1) True clinical pregnancy rate: The auc of the Gardner model in the training set and test set was 0.622 and 0.615; adding "contact area of ​​ICM and TE" on the basis of Gardner parameters, the model performance was improved to 0.633 on the training set, and decreased to 0.614 on the test set; adding "contact ratio of ICM and TE" on the basis of Gardner parameters, the model performance was improved to 0.668 and 0.624; adding "distance between ICM and TE" on the basis of Gardner parameters, the model performance was improved to 0.662 on the training set, and decreased to 0.612 on the test set; the results are summarized in Table 1;

[0081] 2) Actual clinical live birth rate: The auc of the Gardner model in the training set and test set was 0.603 and 0.624; adding "ICM and TE contact area (grouping)" to the Gardner parameters, the model performance was improved to 0.625 and 0.629; adding "ICM and TE contact ratio" to the Gardner parameters, the model performance was improved to 0.627 on the training set and decreased to 0.608 on the test set; adding "ICM and TE distance" to the Gardner parameters, the model performance was improved to 0.648 and 0.630; the results are summarized in Table 2;

[0082] 3) Actual clinical miscarriage rate: The auc of the Gardner model in the training set and test set was 0.565 and 0.604; adding "contact area of ​​ICM and TE" on the basis of Gardner parameters, the model performance was improved to 0.656 and 0.613; adding "contact ratio of ICM and TE" on the basis of Gardner parameters, the model performance was improved to 0.710 and 0.688; adding "distance between ICM and TE" on the basis of Gardner parameters, the model performance was improved to 0.709 on the training set and decreased to 0.648 on the test set; the results are summarized in Table 3.

[0083] Model (True Clinical Pregnancy Rate) Auc: training set Auc: test set Gardner 0.622 0.615 Contact area of ​​Gardner+ICM and TE 0.633 0.614 Gardner+ICM and TE contact ratio 0.668 0.624 Distance between Gardner+ICM and TE 0.662 0.612

[0084] Table 1

[0085] Model (True clinical live birth rate) Auc: training set Auc: test set Gardner 0.603 0.624 Contact area of ​​Gardner+ICM and TE (grouping) 0.625 0.629 Gardner+ICM and TE contact ratio 0.627 0.608 Distance between Gardner+ICM and TE 0.648 0.63

[0086] Table 2

[0087] Model (real clinical miscarriage rate) Auc: training set Auc: test set Gardner 0.565 0.604 Contact area of ​​Gardner+ICM and TE 0.656 0.613 Gardner+ICM and TE contact ratio 0.71 0.688 Distance between Gardner+ICM and TE 0.709 0.648

[0088] Table 3

[0089] The embryo quality assessment method provided in the embodiment of the present application firstly performs feature analysis on clinical embryo data to obtain the correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters; secondly, constructs sample data according to the correlation; finally, uses the sample data for model training to obtain an embryo quality assessment model. In this way, by adding the spatial mutual features of at least two dimensions and the correlation between the preset clinical quality assessment parameters to the sample data, the performance of the embryo quality assessment model is improved, so that the quality of the embryo to be assessed can be more accurately assessed.

[0090] The following is an example to explain the process of obtaining the association relationship in the above embodiment. The preset clinical quality assessment parameters include: at least two preset clinical quality assessment parameters of the real clinical live birth rate, the real clinical miscarriage rate, and the real clinical pregnancy rate; the above step 810 may further include the following steps:

[0091] The clinical embryo data are subjected to feature analysis to obtain a set of correlation relationships between at least two-dimensional spatial mutual features and at least two preset clinical quality assessment parameters.

[0092] Among them, the real clinical live birth rate, also known as the live birth rate, includes the live birth rate per egg retrieval cycle and the live birth rate per transplantation cycle. The live birth rate per egg retrieval cycle refers to the percentage of cycles with live births to the number of egg retrieval cycles. The live birth rate per transplantation cycle refers to the percentage of cycles with live births to the total number of transplantation cycles. The live birth rate is calculated as: number of live births / number of transplantations*100%.

[0093] The true clinical miscarriage rate refers to the proportion of miscarriages that occur after pregnancy is confirmed by routine clinical means. Specifically, it reflects the frequency of miscarriages during pregnancy after the true clinical pregnancy rate is confirmed. This indicator is of great significance for evaluating women's reproductive health status, the quality of reproductive health services, and formulating relevant policies and intervention measures.

[0094] The true clinical pregnancy rate refers to the percentage of the number of cycles with the true clinical pregnancy rate to the total number of cycles of embryo transfer. 7-10 weeks after the menstruation stops, a vaginal B-ultrasound examination is performed. If the gestational sac and primitive heart tube pulsation are visible, it is called the true clinical pregnancy rate. The calculation method of the pregnancy rate is: number of pregnancies / number of transfers*100%.

[0095] Since the preset clinical outcomes include at least two clinical outcomes of the real clinical live birth rate, the real clinical abortion rate, and the real clinical pregnancy rate, the association relationship is at least two groups. Specifically, when the preset clinical outcomes include two clinical outcomes of the real clinical live birth rate, the real clinical abortion rate, and the real clinical pregnancy rate, the association relationship is two groups; illustratively, when the clinical outcomes include the real clinical live birth rate and the real clinical abortion rate, the association relationship includes the association relationship between the cell distribution characteristics of at least two dimensions and the real clinical live birth rate, and the association relationship between the cell distribution characteristics of at least two dimensions and the real clinical abortion rate.

[0096] Furthermore, when the clinical outcomes include true clinical live birth rate, true clinical abortion rate, and true clinical pregnancy rate, the association relationship includes the association relationship between the cell distribution characteristics of at least two dimensions and the true clinical live birth rate, the association relationship between the cell distribution characteristics of at least two dimensions and the true clinical abortion rate, and the association relationship between the cell distribution characteristics of at least two dimensions and the true clinical pregnancy rate.

[0097] Exemplarily, the correlation between the contact area and contact ratio of the inner cell mass and trophoblast cells and the preset clinical outcomes is: in live embryos, the contact surface between ICM and TE is larger. Specifically, when the contact surface is >1196.4um2, the true clinical live birth rate of blastocysts is higher and the miscarriage rate is lower (p<0.01 and p<0.05); when the contact area between ICM and TE accounts for 1.34%-2.7% of the surface area of ​​ICM, the blastocyst has the highest true clinical pregnancy rate (p<0.01); from a biological perspective, a larger contact surface can provide more opportunities for the exchange of nutrients between ICM and TE; at the same time, the contact surface is also an important area for signal transduction between ICM and TE. Through the signal transmission in this area, ICM and TE can coordinate each other's development and differentiation; in addition, the contact surface is also an indication of the stability of the blastocyst outcome, which directly affects further development and implantation in the uterus.

[0098] Exemplarily, the correlation between the distance between the inner cell mass and the trophoblast cells and the preset clinical outcomes is: compared with embryos that are not pregnant or not born alive, the spatial distance between ICM and TE is larger in pregnant and live-born embryos; specifically, when the spatial distance between ICM and TE is greater than 0.56, the live birth rate of blastocysts is the highest (p < 0.05) and the miscarriage rate is the lowest (p < 0.001).

[0099] The embryo quality assessment method provided in the embodiment of the present application has preset clinical outcomes including: at least two clinical outcomes of the real clinical live birth rate, the real clinical miscarriage rate, and the real clinical pregnancy rate; the clinical embryo data is subjected to feature analysis to obtain a set of correlation relationships between the spatial mutual features of at least two dimensions and at least two preset clinical quality assessment parameters. In this way, by determining the correlation relationship between the spatial mutual features of at least two dimensions and at least two preset clinical quality assessment parameters, sample data can be accurately constructed, thereby effectively training the embryo quality assessment model to be trained.

[0100] The feature analysis process in the above embodiment is explained as follows with reference to examples. Fig. 9 This is a schematic diagram of the process of the embryo quality assessment method provided in the embodiment of the present application. Fig. 9 As shown, the above step 810 may further include the following steps 910 and 920:

[0101] Step 910: perform feature classification on the clinical embryo data to obtain a clinical space mutual feature data set.

[0102] Among them, this step is to classify the clinical embryo data according to (based on) the characteristics to obtain a clinical space mutual feature data set; wherein the clinical space mutual feature data set includes multiple clinical space mutual features.

[0103] Step 920: Perform feature analysis on the clinical spatial mutual feature data set to obtain the correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters.

[0104] The feature analysis can be understood as the correlation analysis between the features and the preset clinical quality assessment parameters. Specifically, it is to analyze the correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters.

[0105] The embryo quality assessment method provided in the embodiment of the present application firstly performs feature classification on clinical embryo data to obtain a clinical spatial mutual feature data set; secondly, performs feature analysis on the clinical spatial mutual feature data set to obtain the correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters. In this way, the clinical spatial mutual feature data set required can be accurately obtained through feature classification; furthermore, feature analysis is performed on the cell distribution characteristics to obtain the correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters.

[0106] After introducing the embryo quality assessment method according to the exemplary embodiment of the present disclosure, next, reference is made to Fig.10 An embryo quality assessment device 1000 according to an exemplary embodiment of the present disclosure will be described.

[0107] refer to Fig.10 The embryo quality assessment device 1000 includes: an acquisition module 1010, configured to obtain a three-dimensional reconstructed model of the embryo to be assessed at a preset cultivation stage; a feature extraction module 1020, configured to extract features from the three-dimensional reconstructed model to obtain spatial mutual features of trophoblast cells and inner cell mass in the embryo to be assessed; a prediction module 1030, configured to perform quality assessment on the embryo to be assessed using a preset embryo quality assessment model based on the spatial mutual features, and obtain a predicted value of a quality assessment parameter of the embryo to be assessed.

[0108] In one embodiment, the spatial mutual characteristics include: spatial mutual characteristics of at least two dimensions; the prediction module 1030 is configured to: based on the spatial mutual characteristics of at least two dimensions, use a preset embryo quality assessment model to perform quality assessment on the embryo to be assessed, and obtain a predicted value of the quality assessment parameter of the embryo to be assessed.

[0109] In one embodiment, the embryo quality assessment device 1000 also includes a model training module, which is configured to: perform feature analysis on clinical embryo data to obtain the correlation between spatial mutual features of at least two dimensions and preset clinical outcomes; construct sample data based on the correlation; and use the sample data to perform model training to obtain an embryo quality assessment model.

[0110] In one embodiment, the preset clinical quality assessment parameters include: at least two preset clinical quality assessment parameters of true clinical live birth rate, true clinical miscarriage rate, and true clinical pregnancy rate; the model training module is configured to: perform feature analysis on clinical embryo data to obtain a set of correlation relationships between spatial mutual features of at least two dimensions and at least two preset clinical quality assessment parameters.

[0111] In one embodiment, the spatial mutual characteristics of at least two dimensions include: the distance between the inner cell mass and the trophoblast cells, the contact area between the inner cell mass and the trophoblast cells, and the contact ratio between the inner cell mass and the trophoblast cells; wherein the distance is used to characterize the degree of squeezing between the inner cell mass and the trophoblast cells; and the contact ratio is: the ratio of the contact area to the surface area of ​​the inner cell mass of the three-dimensional reconstructed model.

[0112] In one embodiment, the model training module is configured to: perform feature classification on clinical embryo data to obtain a clinical spatial mutual feature data set; perform feature analysis on the clinical spatial mutual feature data set to obtain a correlation between spatial mutual features of at least two dimensions and preset clinical quality assessment parameters.

[0113] The above-mentioned device is used to execute the method provided by the aforementioned embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0114] The above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASICs), or one or more microprocessors, or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0115] Fig.11 A schematic diagram of a computer device provided in an embodiment of the present application. The device can be integrated into a terminal device or a chip of a terminal device. The terminal can be a computing device with data processing capabilities.

[0116] The device includes: a processor 1101 , a storage medium 1102 , and a bus 1103 .

[0117] The storage medium 1102 stores program instructions executable by the processor 1101. When the computer device 1100 is running, the processor 1101 communicates with the storage medium 1102 via the bus 1103. The processor 1101 executes the program instructions to perform the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0118] Optionally, the present invention also provides a program product, such as a computer-readable storage medium, comprising a program, which is used to execute the above method embodiment when executed by a processor.

[0119] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0122] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), disk or optical disk and other media that can store program codes.

[0123] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating embryo quality, characterized in that: include: Obtain a three-dimensional reconstruction model of the embryo to be evaluated at a preset cultivation stage; Extracting features from the three-dimensional reconstructed model to obtain spatial mutual features of trophoblast cells and inner cell mass in the embryo to be evaluated; According to the spatial mutual characteristics, a preset embryo quality assessment model is used to perform quality assessment on the embryo to be assessed, and a predicted value of the quality assessment parameter of the embryo to be assessed is obtained.

2. The method according to claim 1, characterized in that: The spatial mutual characteristics include: spatial mutual characteristics of at least two dimensions; the quality assessment of the embryo to be assessed is performed using a preset embryo quality assessment model according to the spatial mutual characteristics to obtain a predicted value of the quality assessment parameter of the embryo to be assessed, including: According to the spatial mutual characteristics of the at least two dimensions, a preset embryo quality assessment model is used to perform quality assessment on the embryo to be assessed, so as to obtain a predicted value of a quality assessment parameter of the embryo to be assessed.

3. The method according to claim 2, characterized in that: Before performing quality assessment on the embryo to be assessed using a preset embryo quality assessment model based on the spatial mutual characteristics of the at least two dimensions to obtain a predicted value of the quality assessment parameter of the embryo to be assessed, the method further comprises: Perform feature analysis on clinical embryo data to obtain the correlation between spatial mutual features in at least two dimensions and preset clinical quality assessment parameters; Constructing sample data according to the association relationship; The sample data is used to perform model training to obtain the embryo quality assessment model.

4. The method according to claim 3, characterized in that: The preset clinical quality assessment parameters include: at least two preset clinical quality assessment parameters of true clinical live birth rate, true clinical abortion rate, and true clinical pregnancy rate; The feature analysis of the clinical embryo data to obtain the correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters includes: The clinical embryo data is subjected to feature analysis to obtain spatial mutual features in at least two dimensions and a set of correlation relationships between the at least two preset clinical quality assessment parameters.

5. The method according to claim 2, characterized in that: The at least two-dimensional spatial mutual characteristics include: the distance between the inner cell mass and the trophoblast cells, the contact area between the inner cell mass and the trophoblast cells, and the contact ratio between the inner cell mass and the trophoblast cells; The distance is used to characterize the degree of squeezing between the inner cell mass and the trophoblast cells; the contact ratio is: the ratio of the contact area to the surface area of ​​the inner cell mass of the three-dimensional reconstructed model.

6. The method according to claim 3, characterized in that: The feature analysis of the clinical embryo data to obtain the correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters includes: Performing feature classification on the clinical embryo data to obtain a clinical space mutual feature data set; The clinical spatial mutual feature data set is subjected to feature analysis to obtain a correlation between the spatial mutual features of at least two dimensions and the preset clinical quality assessment parameters.

7. An embryo quality assessment device, characterized in that: include: An acquisition module is configured to acquire a three-dimensional reconstruction model of the embryo to be evaluated at a preset cultivation stage; A feature extraction module is configured to extract features from the three-dimensional reconstruction model to obtain spatial mutual features of trophoblast cells and inner cell mass in the embryo to be evaluated; The prediction module is configured to perform quality assessment on the embryo to be assessed based on the spatial mutual characteristics using a preset embryo quality assessment model to obtain a predicted value of a quality assessment parameter of the embryo to be assessed.

8. A computer device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores program instructions executable by the processor, and when the computer device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of the embryo quality assessment method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the embryo quality assessment method according to any one of claims 1 to 6 are executed.

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