METHODS AND SYSTEMS FOR EMBRYO CLASSIFICIATION

FIELD OF THE INVENTION

The invention relates to methods and systems for improvements to classification of embryos using morpho-kinetic and morphological signatures.

BACKGROUND

In recent years, there have been several advancements in treatments for infertility. Among these treatments are in-vitro fertilization (“IVF”). IVF involves the fertilization of an egg outside the body of a woman. The fertilized egg, once determined to be viable embryo, is then implanted back into the woman. Prior to implantation, multiple tests and analysis may be performed on the embryo in order to assess viability. Such tests may include preimplantation genetic diagnosis (“PGD”) and preimplantation genetic screening (“PGS”). The embryo may also undergo a manual analysis of its morphological and morpho-kinetic parameters based on images of the embryo in order to ensure the embryo is developing. For example, incubators exist that may image embryos during their development while maintaining optimal culture conditions. Embryologist may then evaluate the embryo's development based on its morphological properties at a single time point in the image. Nonetheless, properly selecting an embryo for implantation remains a difficult task.

SUMMARY

Methods and systems are described herein for improvements in embryo selection as well as other classifications of embryos. 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 (or other classifications). For example, the presence of different visual indicators (e.g., corresponding to different morpho-kinetic events) successfully appearing within a series of respective periods of time provides a better predictor of the viability of the embryo than the presence of a single visual indicator (e.g., a single morphological feature) at a single time period because the series of visual indicators allows for the analysis of the growth rate of the embryo as well as an assessment of the trajectory of development. Additionally, for each of these images, the system may assign a probability (e.g., expressed as a float value) that the given image corresponds to the appearance of a particular visual indicator (e.g., corresponding to the embryo entering a new developmental state). As the system analyzes a series of images, the individual morpho-kinetic events result in a profile of the different morpho-kinetic events over the development of the embryo. The system may then analyze these resulting profiles of morpho-kinetic events, which act as a morpho-kinetic signature of the development of the embryo.

To recognize the appearance of different visual indicators and to correctly assess the morpho-kinetic signatures, the system may use a deep learning model that has been trained on annotated data. The annotated data may comprise static images at different development stages. The trained deep learning model may then assess the morpho-kinetic event for each image in a series of images for an embryo. The assessment may be represented as a float-point number in a vector, which, in some embodiments, may correspond to a likelihood that the embryo has reached the given development stage. Furthermore, as the trained deep learning model has provided a probability and/or morpho-kinetic event for each image in a set of images, the system can generate morpho-kinetic signature that is a representation of the morpho-kinetic events in the embryo as a function of time. This morpho-kinetic signature may be represented numerically (e.g., as vector array) or visually.

The morpho-kinetic signature itself may then be used as an input to a second deep learning model or other machine learning model that is used to recognize an implantation quality, a preimplantation genetic screening result, a likelihood of viability, to predict the future development of a morphological feature, or determine another classification for the embryo (e.g., weight, gender, etc.) based on its morpho-kinetic signature. For example, the second deep learning model may be used to determine an optimal time to transfer and/or implant an embryo into a patient based on the morpho-kinetic signature indicating a maximum probability of success. As the morpho-kinetic signature is itself a representation of the morpho-kinetic development of an embryo as a function of time, it provides a mechanism for assessing the development of the embryo as a whole. Thus, by generating a morpho-kinetic signature using a first deep learning model, the system may then input the morpho-kinetic signature into a second deep learning model to obtain an assessment of the embryo that provides a better prediction than any assessment based on a static image (and/or individual morpho-kinetic events).

Methods and systems are described herein for improvements in embryo selection as well as other classifications of embryos. These improvements are achieved by development of a system that utilizes two artificial neural networks to improve predicting whether an embryo will be more or less likely to be successfully implanted. For example, the current state of the art suffers from numerous problems that hinder a practitioner's ability to predict whether an implantation will be successful. First, conventional PGS testing is used to classify whether an embryo will be successfully implanted based solely on chromosome count (e.g., whether the embryo is euploid or aneuploid). In contrast, the methods and systems use other factors such as the morphokinetics or morphology of an embryo to determine whether an embryo can be successfully implanted.

To address these technical problems, the disclosed methods and algorithms utilize supplementing embryo classification as (an)euploid with known implantation data. The artificial neural network leverages this empirical data that is available for embryos to improve predictions of successful implantation over current methods that rely on (an)euploid classification alone. Further, some embodiments have another artificial neural network that can permit classification without requiring patients to undergo PGS testing. Thus, by training the system to utilize these inputs, an implantation potential can be determined and provided by the disclosed neural network(s).

In one aspect, a system may include software configured for operations including receiving of a first feature input, wherein a first feature input is based on a morphokinetic signature of an embryo. The software operations may also include determining a first feature output based on a classification of the embryo as euploid or aneuploid. Also, the software operations may include inputting the first feature output and the first feature input into a second artificial neural network to generate a second feature output based on a predicted implantation potential of the embryo, wherein the second artificial neural network is trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures and known implantation data. The system may also generate for display, on a user interface, a recommendation for implantation based on the second feature output.

Various other aspects, features, and advantages of the invention will be apparent through the detailed description of the invention and the drawings attached hereto. It is also to be understood that both the foregoing general description and the following detailed description are examples and not restrictive of the scope of the invention. As used in the specification and in the claims, the singular forms of “a.” “an,” and “the” include plural referents unless the context clearly dictates otherwise. In addition, as used in the specification and the claims, the term “or” means “and/or” unless the context clearly dictates otherwise. Additionally, as used in the specification “a portion,” refers to a part of, or the entirety of (i.e., the entire portion), a given item (e.g., data) unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a diagram of the morphological changes undergone by an embryo during development, in accordance with one or more embodiments.

FIG. 2 shows a flowchart of the steps involved in classifying morphological and morpho-kinetic features, in accordance with one or more embodiments.

FIG. 3 shows an illustrative system upon which to implement methods and systems for embryo classification using morpho-kinetic signatures, in accordance with one or more embodiments.

FIG. 4 shows a neural network used for classifying embryos, in accordance with one or more embodiments.

FIG. 5 shows an exemplary visual representation of a morpho-kinetic signatures based on a first set of vectors, in accordance with one or more embodiments.

FIG. 6 shows an exemplary visual representation of a morpho-kinetic signatures based on a second set of vectors, in accordance with one or more embodiments.

FIG. 7 shows a flowchart of the steps involved in embryo classification using morpho-kinetic signatures, in accordance with one or more embodiments.

FIG. 8 shows a flowchart of the steps involved in generating morpho-kinetic signatures for embryos, in accordance with one or more embodiments.

FIG. 9 shows a flowchart of the steps involved in using morpho-kinetic signatures to predict viability of embryos, in accordance with one or more embodiments.

FIG. 10 shows a user interface for a system for filtering user data related to embryonic implantation, in accordance with one or more embodiments.

FIG. 11 shows a user interface for a system for summarizing user data related to embryonic implantation, in accordance with one or more embodiments.

FIG. 13 shows a user interface for a system for generating for display information related to identifying optimal decision times for embryonic implantation, in accordance with one or more embodiments.

FIG. 14 shows an illustrative graph related to an implantation score as a function of time for use as identifying an optimal time for transferring an embryo, in accordance with one or more embodiments.

FIG. 15 shows an illustrative graph related to a blastulation score as a function of time for use as identifying an optimal time for transferring an embryo, in accordance with one or more embodiments.

FIG. 16 shows an illustrative graph identifying an optimal time for transferring an embryo, in accordance with one or more embodiments.

FIG. 17 shows a flowchart for a method of identifying endometrial thickness, in accordance with one or more embodiments.

FIG. 18 shows a flowchart for a method of selecting embryos based on predictive positive value scores, in accordance with one or more embodiments.

FIG. 19 shows a flowchart for a method of predicting a time of peak implantation success, in accordance with one or more embodiments.

FIG. 20 shows a block diagram for determining a predicted implantation potential of an embryo based on PGS data and morphokinetic signatures, in accordance with one or more embodiments.

FIG. 21 shows a block diagram for determining a predicted implantation potential of an embryo based on morphokinetic signatures, in accordance with one or more embodiments.

FIG. 22 shows a flowchart for a method of determining a predicted implantation potential of an embryo, in accordance with one or more embodiments.

DETAILED DESCRIPTION OF THE DRAWINGS

In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be appreciated, however, by those having skill in the art, that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other cases, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention. It should also be noted that the embodiments herein may be based on morpho-kinetic/morphology and in general any AI based model as described herein. For example, an embodiment described using a model based on morpho-kinetic characteristics may likewise be based on morphologic characteristics.

FIG. 1 shows a diagram of the morphological changes undergone by an embryo during development, in accordance with one or more embodiments. For example, diagram 100 includes images of an embryo undergoing different morpho-kinetic events. As described herein, a morpho-kinetic event may include the appearance of a morphological feature and/or the achievement of a morphological stage. For example, a morphological feature may include any feature relating to a form, composition, or structure of an embryo, and/or portion of an embryo. For example, a morphological feature may include one or more cell splits (e.g., cell splits one through nine), the development of morula, the start of blastulation, and the pronuclei appearance and fading. In another example, a morphological feature and/or achievement of a morphological stage may correspond to one or more of the Gardner expansion degrees: (1) early blastocyst (e.g., where the blastocoel formed less than half of the volume of the embryo); (2) blastocyst (e.g., where the blastocoel formed more than half of the volume of the embryo); (3) full blastocyst (e.g., where the blastocoel completely filled the embryo): (4) expanded blastocyst (e.g., where the blastocoel volume was larger than that of the early embryo, and the zona had begun to thin): (5) hatching blastocyst (e.g., where the trophectoderm had begun to herniate though the zona): and (6) hatched blastocyst (e.g., where the blastocyst had completely escaped from the zona).

In some embodiments, whether or not a morphological feature is present may depend on when the morphological feature is first distinguishable (e.g., a first appearance) or when the morphological feature is clearly separated from other features of a cell. In some embodiments, morphological features and/or achievements of a morphological stage may depend on whether or not a particular threshold is met. For example, the threshold may be keyed to a particular; (a) fragmentation percent at two cells: (b) fragmentation percent at four cells: (c) fragmentation percent at eight cells: (d) blastomers symmetry at two cells: (e) blastomers symmetry at four cells: (f) blastomers symmetry at eight cells: (g) inner cell mass: (h) trophectoderm: (i) cavity shape and area: or (j) zona pellucida thickness.

As shown in diagram 100, an embryo is transitioning through a series of morpho-kinetic events such as a two cell split (e.g., event 102), four cell split (e.g., event 104), eight cell split (e.g., event 106), morula development (e.g., event 108), blastocyst development (e.g., event 110). In some embodiments, each of the images in diagram 100 may be a single view and/or focal plane of the embryo at a given time point. For example, in some embodiments, the system may generate an image set of seven focal planes (e.g., to generate a three-dimensional view of the embryo). Furthermore, the seven images of diagram 100 may comprise only individual instances of a time-lapse video of the development of the embryo.

In some embodiments, the system may determine temporal distributions of morpho-kinetic events and time intervals between the consecutive events as determined both in manually annotated data and an artificial neural network (e.g., as described in FIG. 2). For example, using thousands of annotated profiles, the system may train a artificial neural network to successfully identify the morpho-kinetic event and time intervals between. For example, diagram 100 may represent one of 11,000 time-lapse video files of embryos cultured in one or more incubators. The embryos may be imaged using z-stacks, 10-20 μm apart, and may be recorded at ten to twenty minute intervals for one to six days. Morpho-kinetic events of all embryos may be annotated by embryologists in accordance with established protocols. The embryos may be divided into train, validation, and test sets. Test sets may consist of randomly selected subset of ten to twenty percent of all embryos. Test sets were strictly maintained uncontaminated and were used only after training processes were completed.

FIG. 2 shows a flowchart of the steps involved in classifying morphological and morpho-kinetic features, in accordance with one or more embodiments. Process 200 may be performed using the control circuitry of one or more components described in FIG. 3. For example, process 200 may relate to the use of an artificial neural network that has been trained on annotated data. For example, the annotated data may comprise static images at different development stages of an embryo (e.g., as shown in FIG. 1). The trained artificial neural network may then determine the development stage for each image in a series of images of an embryo. The trained artificial neural network may represent a first deep learning model, the outputs of which are used to generate inputs for a second deep learning model, as discussed below in relation to FIG. 7.

At step 202, process 200 receives (e.g., using one or more components of system 300 (FIG. 3)) a first image of a first embryo. For example, in some embodiments, the first image may be based on an image set of a series of images of the embryo (e.g., time-lapse images). The image set may include multiple angles and/or views of the embryo. For example, the images of the embryo may include a Z stack featuring a plurality of frames (e.g., seven). The Z stack may comprise a compilation of images taken at a set interval between the first and last planes of focus.

In some embodiments, the system may generate a first pixel array based on the image of the embryo. For example, in some embodiments, the system may generate pixel arrays to represent the images. The pixel array may refer to computer data that describes the image (e.g., pixel by pixel). In some embodiments, this may include one or more vectors, arrays, and/or matrices that represent either a Red, Green, Blue colored or grayscale images. Furthermore, in some embodiments, the system may additionally convert the image from a set of one or more vectors, arrays, and/or matrices to another set of one or more vectors, arrays, and/or matrices. For example, the system may convert an image set having a red color array, a green color array, and a blue color to a grayscale color array. The array may have a varying number of vectors, and in some embodiments, the vectors may additional include clinical, demographic, or other data related to the embryo, which may be used to train the artificial neural network to identify a morphological and morpho-kinetic feature.

In some embodiments, the data set may include data based on known implantation data (“KID”) embryos. KID embryos are not normally usable for training and testing artificial neural networks used for classifying morphological and morpho-kinetic features because KID embryos also depend on the capacity of the embryo to develop in the incubator under controlled conditions, implantation also depends on uterus receptivity, which is not taken into account in the training process. KID embryos are also preselected for transfer according to morphological and/or morpho-kinetic parameters (e.g., based on manual annotation of the data), which may introduce bias. It should be noted, as described below, through a comparative analysis of morpho-kinetic signature generated (e.g., using the second deep learning model), the viable embryo may in many cases be determined.

At step 204, process 200 labels (e.g., using one or more components of system 300 (FIG. 3)) the first image with a known morphological or morpho-kinetic feature in the first image. For example, the images may be generated about fifteen μm apart and at about twenty minute intervals during incubation of the embryos. This imaging may provide a continuous three-dimensional image of the embryo. The images may comprise a data set of thousands of embryos (e.g., greater than eleven thousand) and cultured in a plurality of incubators at remote locations. In some embodiments, each of these embryos is also tagged with additional clinical and/or other data (e.g., metadata) that is stored with the images. For example, the metadata may include maternal age, co-transferred embryo statistics, and implantation outcome.

At step 206, process 200 trains (e.g., using one or more components of system 300 (FIG. 3)) an artificial neural network to detect the known morphological or morpho-kinetic feature in the first image. For example, the system may train the artificial neural network, including the collection and preprocessing of data, as described in FIG. 4 below. The trained artificial neural network may classify the image of the embryo as corresponding to a morphological and/or a morpho-kinetic feature. For example, the trained artificial neural network may identify morphological features such as: (a) fragmentation percent at two cells; (b) fragmentation percent at four cells: (c) fragmentation percent at eight cells: (d) blastomers symmetry at two cells: (e) blastomers symmetry at four cells: (f) blastomers symmetry at eight cells: (g) inner cell mass: (h) trophectoderm: (i) cavity shape and area: or (j) zona pellucida thickness. Additionally or alternatively, the first trained artificial neural network may identify the beginning and end (e.g., the point of clear separation) of each morpho-kinetic event. That is, the system may identify a plurality of stages for one or more morpho-kinetic events.

For example, the trained artificial neural network may be trained to classify cell splits (e.g., cell splits one through nine), the development of morula, the start of blastulation, and the pronuclei appearance and fading and/or to classify an embryo into the one or more of the Gardner expansion degrees: (1) early blastocyst (e.g., where the blastocoel formed less than half of the volume of the embryo): (2) blastocyst (e.g., where the blastocoel formed more than half of the volume of the embryo): (3) full blastocyst (e.g., where the blastocoel completely filled the embryo): (4) expanded blastocyst (e.g., where the blastocoel volume was larger than that of the early embryo, and the zona had begun to thin): (5) hatching blastocyst (e.g., where the trophectoderm had begun to herniate through the zona): and (6) hatched blastocyst (e.g., where the blastocyst had completely escaped from the zona). For example, the trained artificial neural network may comprise input arrays with vectors corresponding to one or more of the cells splits, the morula development, the start of blastulation, the pronuclei appearance and fading, and the Gardner expansion degrees.

At step 208, process 200 receives (e.g., using one or more components of system 300 (FIG. 3)) a second image of a second embryo. For example, the second image may be an image of an embryo which has an unknown morphological or morpho-kinetic feature. For example, in some embodiments, the second image may be based on an image set of a series of images of the embryo (e.g., time-lapse images). The image set may include multiple angles and/or views of the embryo (e.g., a Z stack of images). In some embodiments, the system may generate a second pixel array based on the image of the embryo. For example, in some embodiments, the system may generate pixel arrays to represent the images. The pixel array may refer to computer data that describes the image (e.g., pixel by pixel). In some embodiments, the system may normalize the second array in order to account for bias in the system.

At step 210, process 200 inputs (e.g., using one or more components of system 300 (FIG. 3)) the second image into the trained artificial neural network. The system may process the input through one or more hidden layers (as described in FIG. 4 below) to classify the input as including a morphological or morpho-kinetic feature.

At step 212, process 200 receives (e.g., using one or more components of system 300 (FIG. 3)) an output from the trained artificial neural network indicating that the second image includes the known morphological or morpho-kinetic feature. For example, the trained artificial neural network may generate an output indicating that the image corresponds to a given class. As described below, this output may itself be used to generate an input (e.g., a second vector array) into a second deep learning model (e.g., a second artificial neural network).

Furthermore, the system may receive a series of images (e.g., a time-lapse of images) of the second embryo. The trained artificial neural network may output a determined morphological or morpho-kinetic feature for each image in the series of images for the embryo. In some embodiments, the output may comprise a float-point number (e.g., between one and zero) corresponding to a probability that the embryo corresponds to a classification of a morphological or morpho-kinetic feature. The system may then compile the series of outputs (e.g., the series of vector arrays each corresponding to a different time point) to generate a representation of the development of the embryo as a function of time. In some embodiments, the series of outputs may be converted into a visual representation (e.g., a heat map) as shown in FIGS. 5-6 below.

FIG. 3 shows an illustrative system upon which to implement methods and systems for embryo classification using morpho-kinetic signatures, in accordance with one or more embodiments. As shown in FIG. 3, system 300 may include user device 322, user device 324, and/or other components. Each user device may include any type of mobile terminal, fixed terminal, or other device. Each of these devices may receive content and data via input/output (hereinafter “I/O”) paths and may also include processors and/or control circuitry to send and receive commands, requests, and other suitable data using the I/O paths. The control circuitry may be comprised of any suitable processing circuitry. Each of these devices may also include a user input interface and/or display for use in receiving and displaying data.

By way of example, user device 322 and user device 324 may include a desktop computer, a server, or other client device. Users may, for instance, utilize one or more of the user devices to interact with one another, one or more servers, or other components of system 300. It should be noted that, while one or more operations are described herein as being performed by particular components of system 300, those operations may, in some embodiments, be performed by other components of system 300. As an example, while one or more operations are described herein as being performed by components of user device 322, those operations may, in some embodiments, be performed by components of user device 324. System 300 also includes machine learning model 302, which may be implemented on user device 322 and user device 324, or accessible by communication paths 328 and 330, respectively. It should be noted that, although some embodiments are described herein with respect to machine learning models, other prediction models (e.g., statistical models or other analytics models) may be used in lieu of, or in addition to, machine learning models in other embodiments (e.g., a statistical model replacing a machine learning model and a non-statistical model replacing a non-machine learning model in one or more embodiments).

Each of these devices may also include memory in the form of electronic storage. The electronic storage may include non-transitory storage media that electronically stores information. The electronic storage of media may include: (i) system storage that is provided integrally (e.g., substantially non-removable) with servers or client devices; and/or (ii) removable storage that is removably connectable to the servers or client devices via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storages may include optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storages may include virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). The electronic storage may store software algorithms, information determined by the processors, information obtained from servers, information obtained from client devices, or other information that enables the functionality as described herein.

FIG. 3 also includes communication paths 328, 330, and 332. Communication paths 328, 330, and 332 may include the Internet, a mobile phone network, a mobile voice or data network (e.g., a 4G or LTE network), a cable network, a public switched telephone network, or other types of communications network or combinations of communications networks. Communication paths 328, 330, and 332 may include one or more communications paths, such as a satellite path, a fiber-optic path, a cable path, a path that supports Internet communications (e.g., IPTV), free-space connections (e.g., for broadcast or other wireless signals), or any other suitable wired or wireless communications path or combination of such paths. The computing devices may include additional communication paths linking a plurality of hardware, software, and/or firmware components operating together. For example, the computing devices may be implemented by a cloud of computing platforms operating together as the computing devices.

As an example, with respect to FIG. 3, machine learning model 302 may take inputs 304 and provide outputs 306. The inputs may include multiple data sets such as a training data set and a test data set. In some embodiments, outputs 306 may be fed back to machine learning model 302 as input to train machine learning model 302 (e.g., alone or in conjunction with user indications of the accuracy of outputs 306, labels associated with the inputs, or with other reference feedback information). In another embodiment, machine learning model 302 may update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction (e.g., outputs 306) and reference feedback information (e.g., user indication of accuracy, reference labels, or other information). In another embodiment, where machine learning model 302 is a neural network, connection weights may be adjusted to reconcile differences between the neural network's prediction and the reference feedback. In a further use case, one or more neurons (or nodes) of the neural network may require that their respective errors are sent backward through the neural network to them to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model 302 may be trained to generate better predictions.

In some embodiments, machine learning model 302 may include an artificial neural network. In such embodiments, machine learning model 302 may include an input layer and one or more hidden layers. Each neural unit of machine learning model 302 may be connected with many other neural units of machine learning model 302. Such connections can be enforcing or inhibitory in their effect on the activation state of connected neural units. In some embodiments, each individual neural unit may have a summation function which combines the values of all of its inputs together. In some embodiments, each connection (or the neural unit itself) may have a threshold function that the signal must surpass before it propagates to other neural units. Machine learning model 302 may be self-learning and trained, rather than explicitly programmed, and can perform significantly better in certain areas of problem solving, as compared to traditional computer programs. During training, an output layer of machine learning model 302 may correspond to a classification of machine learning model 302 and an input known to correspond to that classification may be input into an input layer of machine learning model 302 during training. During testing, an input without a known classification may be input into the input layer, and a determined classification may be output.

In some embodiments, machine learning model 302 may include multiple layers (e.g., where a signal path traverses from front layers to back layers). In some embodiments, back propagation techniques may be utilized by machine learning model 302 where forward stimulation is used to reset weights on the “front” neural units. In some embodiments, stimulation and inhibition for machine learning model 302 may be more free-flowing, with connections interacting in a more chaotic and complex fashion. During testing, an output layer of machine learning model 302 may indicate whether or not a given input corresponds to a classification of machine learning model 302. Machine learning model 302 may be used to classify morphological and morpho-kinetic features. For example, the machine learning model 302 may input an image (or images) of an embryo and receive an output classifying the embryo as corresponding to morphological and morpho-kinetic features.

In some embodiments, system 300 may use simulated labels and/or bootstrap labels to refine and/or improve one or more models described herein. For example, system 300 may simulate labels for known implantation data that is partial or incomplete. In the case of simulated labels, system 300 may receive embryo data (e.g., known implantation data) that include images of the embryos' development (e.g., time-lapse images), but does not include labeled classifications corresponding to the embryos' development (e.g., an implantation quality, a preimplantation genetic screening result, a likelihood of viability, a prediction of the future development of a morphological feature, or another classification for the embryo (e.g., weight, gender, etc.).

The system may generate the labeled classification based on a review of the morpho-kinetic signatures of the embryos (e.g., as generate by system 300 using the images of an embryo's development) as well as information about the viability of additional embryos implanted with the embryo. For example, if the embryo was implanted with one other embryo, and only one embryo resulted in a viable embryo, the system may compare the morpho-kinetic signatures of the embryos. If one of the morpho-kinetic signatures has a high predicted score (e.g., indicating a high probability of viability) and the other morpho-kinetic signature has a low predicted score (e.g., indicating a low probability of viability of the embryos), the system may generate a simulated label that the embryo with the morpho-kinetic signature that has a high predicted score as viable and/or that the embryo with the morpho-kinetic signature that has a low predicted score as not viable. This information may be used to grow a data set of information that may be used to train, and further improve, an artificial neural network (e.g., as described in FIGS. 7-9).

Additionally or alternatively, system 300 may generate additional training data through the use of bootstrap aggregation. For example, system 300 may access embryo data (e.g., known implantation data) that has no labels. System 300 may then apply a bootstrapping model to the unlabeled data to identify embryos with a predicted classification that is above a threshold confidence. For example, system 300 may receive data for 1,000 embryos. System 300 may generate morpho-kinetic signatures for the embryos, but have no information on one or more classifications (e.g., in contrast to the simulated labels above). System 300 may however apply a model to the morpho-kinetic signatures for the embryos that determine, with a predetermined level of confidence, the morpho-kinetic signatures for the embryos that resulted in a given classification (e.g., a viable embryo) based on the currently available training data. If 100 embryos meet the threshold, the 100 embryos may be added to the training data.

For example, system 300 may generate a prediction score (e.g., corresponding to a float-point value) indicating a probability of a given morpho-kinetic signature corresponding to a given classification. System 300 may then compare the prediction scores to the prediction scores of existing training data. System 300 may determine a threshold score above which any prediction score corresponds to a given classification (e.g., a viable embryo). In response to identifying morpho-kinetic signatures with a prediction score above this threshold, system 300 may designate these morpho-kinetic signatures as corresponding to the classification. System 300 may then add these morpho-kinetic signatures with their labeled classification to the training set of data.

System 300 may iteratively adjust the model based on additional data to continually lower the threshold score above which any prediction score corresponds to the given classification. For example, as system 300 receives more training data, system 300 may refine the artificial neural network and/or other machine learning algorithm to better predict whether or not a given morpho-kinetic signature corresponds to a given classification.

FIG. 4 shows a graphical representations of artificial neural network models for classifying embryos, in accordance with one or more embodiments. Model 400 illustrates an artificial neural network. Model 400 includes input layer 402. Images (or vector arrays based on images) may be entered into model 400 at this level. Model 400 also includes one or more hidden layers (e.g., hidden layers 404, 406, and 408). Model 400 may be based on a large collection of neural units (or artificial neurons). Model 400 loosely mimics the manner in which a biological brain works (e.g., via large clusters of biological neurons connected by axons). Each neural unit of a model 400 may be connected with many other neural units of model 400. Such connections can be enforcing or inhibitory in their effect on the activation state of connected neural units. In some embodiments, each individual neural unit may have a summation function which combines the values of all of its inputs together. In some embodiments, each connection (or the neural unit itself) may have a threshold function that the signal must surpass before it propagates to other neural units. Model 400 may be self-learning and trained, rather than explicitly programmed, and can perform significantly better in certain areas of problem solving, as compared to traditional computer programs. During training, output layer 410 may correspond to a classification of model 400 (e.g., whether or not a given image set corresponds to a genotype biomarker) and an input known to correspond to that classification may be input into input layer 402. In some embodiments, model 400 may include multiple layers (e.g., where a signal path traverses from front layers to back layers). In some embodiments, back propagation techniques may be utilized by model 400 where forward stimulation is used to reset weights on the “front” neural units. In some embodiments, stimulation and inhibition for model 400 may be more free-flowing, with connections interacting in a more chaotic and complex fashion. Model 400 also includes output layer 410. During testing, output layer 410 may indicate whether or not a given input corresponds to a classification of model 400 (e.g., whether or not an image corresponds to a morphological and/or morpho-kinetic feature and/or a morpho-kinetic signature corresponds to a viable embryo).

In some embodiments, embryo video files may undergo preprocessing. For example, the system may reduce the size of images by identifying and discarding empty well images, cropping the images to portions that contain the embryos, and down-sampling the cropped images. In some embodiments, the system may also automatically determine whether or not a well includes an embryo. For example, the system may determine in each pixel, the 2-norm of the absolute values of both channels is calculated to generate a gradient map which is then normalized by the median gradient value.

Model 400 may be a convolutional neural network (“CNN”). The convolutional neural network is an artificial neural network that features one or more convolutional layers. Convolution layers extract features from an input image. Convolution preserves the relationship between pixels by learning image features using small squares of input data. For example, the relationship between the individual parts of the image of the embryo and/or morpho-kinetic signature may be preserved.

The input of the CNN are preprocessed packets of embryo and time index. The time index correlates the image as a function of time. The CNN architecture consists of multiple hidden layers (e.g., layer 404 through 408). The last layer (e.g., layer 408) may have six input neurons. The output neuron (e.g., output layer 410), which is the packet score, is selected according to the time index of the packet. The packet score creates a numerical representation of the image.

FIG. 5 shows an exemplary visual representation of a morpho-kinetic signature based on a first set of vectors, in accordance with one or more embodiments. For example, image 500, image 520, and image 550 are visual representations (e.g., a heat map) of a morpho-kinetic signature. The x-axis of each image corresponds to time, whereas the y-axis corresponds to a particular morpho-kinetic event (e.g., Gardner expansion degrees). The color variations of each image, as described by the legend, indicates a float value for each morpho-kinetic event (e.g., a float value between zero and one). The float value may be used as an indicator of the probability and/or appearance of indicia of the morpho-kinetic event. Image 500 corresponds to an embryo achieving early blastocyst. Image 520 corresponds to an embryo hatching blastocyst, and image 550 corresponds to an embryo reaching hatched blastocyst.

FIG. 6 shows another exemplary visual representation of a morpho-kinetic signature based on a second set of vectors, in accordance with one or more embodiments. For example, image 600, image 620, and image 650 are visual representations (e.g., a heat map) of a morpho-kinetic signature. In contrast to FIG. 5, the x-axis corresponds morpho-kinetic events such as cell splits, pronuclei appearance, development of morula, and the start of blastulation. For example, image 600 corresponds to an embryo reaching pronuclei appearance. Image 620 corresponds to a ninth cell split, and image 650 corresponds to the start of blastulation.

As shown by FIGS. 5-6, the morpho-kinetic signature of the development of each embryo acts as a fingerprint from which the system may determine one or more classifications. Based on the morpho-kinetic events, including the appearance, rate, and/or combination with additional data (e.g., clinical data, preimplantation genetic screening, etc.), the system may classify a given embryo. In some embodiments, the system may additionally use the morpho-kinetic signatures to predict, when, if, the size of, and/or other characteristics of morpho-kinetic events. For example, using a morpho-kinetic signature of an embryo's pronuclei appearance, the system may determine when the embryo will achieve the ninth cell split. It should also be noted that while FIGS. 5-6 represent visual representations of underlying numerical morpho-kinetic signatures, in some embodiments, the visual representations themselves may act as input to an artificial neural network as described herein.

It should also be noted that FIGS. 5-6 are illustrative only and not meant to be limiting. For example, in some embodiments, the system may use morpho-kinetic signatures with a varying number of vectors. For example, in one embodiment, a morpho-kinetic signature may have eleven vectors corresponding to eleven morphological stages (e.g., each vector corresponding to stage). Alternatively, in one embodiment, a morpho-kinetic signature may have twenty vectors corresponding to thirteen cell splits and six stages of blastocyst.

FIG. 7 shows a flowchart of the steps involved in embryo classification using morpho-kinetic signatures, in accordance with one or more embodiments. For example, process 700 may describe the use of the morpho-kinetic signatures as represented in FIGS. 5-6 as generated by an artificial neural network as described in FIGS. 3-4. In some embodiments, process 700 may be used to classify an inputted morpho-kinetic signature according to its optimal transfer time. In such case, an artificial neural network may be trained to determine a maximum implantation and/or blastulation score as described in FIGS. 14-18 below. For example, the system may receive a first morpho-kinetic signature of a first embryo, wherein the first morpho-kinetic signature is a representation of morpho-kinetic events in the first embryo as a function of time. The system may then label the first morpho-kinetic signature with a known time of peak implantation. The system may then train an artificial neural network to detect the known time of peak implantation based on the first morpho-kinetic signature. The system may then receive a second morpho-kinetic signature of a second embryo with an unknown time of peak implantation, wherein the second morpho-kinetic signature is a representation of morpho-kinetic events in the second embryo as a function of time. The system may then input the second morpho-kinetic signature into the trained artificial neural network. The system may then receive a prediction from the trained artificial neural network that the second morpho-kinetic signature corresponds to the known time of peak implantation.

At step 702, process 700 (e.g., via one or more components of system 300 (FIG. 3)) receives a first morpho-kinetic signature of a first embryo. For example, the first morpho-kinetic signature may be a representation of morpho-kinetic events in the first embryo as a function of time. In one example, the first morpho-kinetic signature may be based on a series of time-lapse images of the morpho-kinetic events in the first embryo (e.g., as captured while the first embryo is being incubated).

In some embodiments, the morpho-kinetic signature may be a vector array that numerically describes the morpho-kinetic events in the first embryo. For example, each of the morpho-kinetic events may be represented as a float value (e.g., from zero to one) in a vector. A value of zero may indicate that the morpho-kinetic event corresponding to the vector is non-present, whereas a value of one may indicate that the morpho-kinetic event is present. The float value may be used to apply a percentage or probability of the morpho-kinetic event occurring.

In some embodiments, a morpho-kinetic event may be an appearance of a morphological features in the first embryo and/or a rate of development for the morphological feature. For example, the morpho-kinetic event may correspond to a cell split (e.g., cell splits one through nine), a development of a morula, a start of blastulation, a pronuclei appearance, or a pronuclei fading. Moreover, the morpho-kinetic event may include a first appearance of a morphological feature among other morphological features and/or a clear separation of the morphological feature from the other morphological features.

Additionally or alternatively, the morpho-kinetic event may be an achievement of a Garner expansion degree such as: (1) early blastocyst (e.g., where the blastocoel formed less than half of the volume of the embryo): (2) blastocyst (e.g., where the blastocoel formed more than half of the volume of the embryo): (3) full blastocyst (e.g., where the blastocoel completely filled the embryo): (4) expanded blastocyst (e.g., where the blastocoel volume was larger than that of the early embryo, and the zona had begun to thin): (5) hatching blastocyst (e.g., where the trophectoderm had begun to herniate though the zona): and (6) hatched blastocyst (e.g., where the blastocyst had completely escaped from the zona).

Additionally or alternatively, the morpho-kinetic event may correspond to a fragmentation percent at two cells, a fragmentation percent at four cells, a fragmentation percent at eight cells, blastomers symmetry at two cells, blastomers symmetry at four cells, blastomers symmetry at eight cells, inner cell mass quality, trophectoderm quality, cavity shape, cavity area, cavity percentage, and/or zona pellucida thickness.

At step 704, process 700 (e.g., via one or more components of system 300 (FIG. 3)) labels the first morpho-kinetic signature with a known classification. For example, the known classification may correspond to an implantation quality, a likelihood of viability, a preimplantation genetic screening result, or a predicted morphological feature. For example, in some embodiments, the labeled classification may correspond to a time (e.g., during incubation) at which a maximum implantation and/or blastulation score (e.g., probability of success) is reached as described in FIGS. 14-18 below.

At step 706, process 700 (e.g., via one or more components of system 300 (FIG. 3)) trains an artificial neural network to detect the known classification based on the first morpho-kinetic signature. For example, the system may use one or more artificial neural networks as described above in FIGS. 3-4. For example, in embodiments in which the artificial neural network is trained to determine a time (e.g., during incubation) at which a maximum implantation and/or blastulation score (e.g., probability of success) is reached as described in FIGS. 14-18 below, the system trains the artificial neural network to determine a peak score (e.g., maximum implantation and/or blastulation score) based on its morpho-kinetic signature. In such a case, known morpho-kinetic signature data (e.g., as shown in FIGS. 14-15) may be used as training data. For each embryo in the training data, a peak score may be identified and the morpho-kinetic signature may be labeled with the time corresponding to the peak score.

At step 708, process 700 (e.g., via one or more components of system 300 (FIG. 3)) receives a second morpho-kinetic signature of a second embryo with an unknown classification. For example, the second morpho-kinetic signature may be a representation of morpho-kinetic events in the second embryo as a function of time. In some embodiments, the system may also receive additional information with the second morpho-kinetic signature. Additional information may include preimplantation genetic diagnosis or preimplantation genetic screening data. Additional information may also include clinical data such as age, weight, body mass index, endometrial thickness as well as other medical and demographic data (e.g., family history, race, etc.). For example, in embodiments in which the artificial neural network is trained to determine a time (e.g., during incubation) at which a maximum implantation and/or blastulation score (e.g., probability of success) is reached as described in FIGS. 14-18 below, the system may receive a morpho-kinetic signature for which an optimal transfer time is needed.

At step 710, process 700 (e.g., via one or more components of system 300 (FIG. 3)) inputs the second morpho-kinetic signature into the trained artificial neural network. In some embodiments, in which the system also uses additional information, the system may label the first morpho-kinetic signature with first additional information about the first embryo, train the artificial neural network to detect the known classification based on the first morpho-kinetic signature and the first additional information, receive second additional information about the second embryo, and input the second additional information along with the second morpho-kinetic signature into the trained artificial neural network to receive the prediction. For example, in embodiments in which the artificial neural network is trained to determine a time (e.g., during incubation) at which a maximum implantation and/or blastulation score (e.g., probability of success) is reached as described in FIGS. 14-18 below, the system may input a morpho-kinetic signature for an embryo into the trained artificial neural network.

At step 712, process 700 (e.g., via one or more components of system 300 (FIG. 3)) receives a prediction from the trained artificial neural network that the second morpho-kinetic signature corresponds to the known classification. For example, the system may provide a prediction that indicates that the embryo corresponding to the second morpho-kinetic signature corresponds to the known classification. For example, the classification may indicate that the embryo has a high implantation quality, a high likelihood of viability, a specific preimplantation genetic screening result, or a predicted morphological feature (or date when a morphological feature will appear). For example, the morpho-kinetic signature may be used to predict when a morphological feature will appear. For example, in embodiments in which the artificial neural network is trained to determine a time (e.g., during incubation) at which a maximum implantation and/or blastulation score (e.g., probability of success) is reached as described in FIGS. 14-18 below, the system may output a classification corresponding to an ideal time at which to transfer an embryo.

For example, the prediction may comprise a prediction score (e.g., a float value between one and zero), which indicates a likelihood of the embryo corresponding to the known classification (e.g., the viability of the embryo if implanted), where a one score indicates a strong correlations (e.g., corresponds to high viability) and a zero score indicates a low correlations (e.g., corresponds to a low viability). Alternatively, the prediction may include a binary determination of whether or not the embryo corresponds to a given classification.

It is contemplated that the steps or descriptions of FIG. 7 may be used with any other embodiment of this disclosure. In addition, the steps and descriptions described in relation to FIG. 7 may be done in alternative orders or in parallel to further the purposes of this disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously to reduce lag or increase the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in relation to FIGS. 1 and 3-4 could be used to perform one or more of the steps in FIG. 7.

FIG. 8 shows a flowchart of the steps involved in generating morpho-kinetic signatures for embryos, in accordance with one or more embodiments. For example, process 800 may described an embodiment for generating the morpho-kinetic signatures used in FIGS. 7 and 9.

At step 802, process 800 (e.g., via one or more components of system 300 (FIG. 3)) receives a first output from an artificial neural network indicating that an embryo has a first classification at a first time point. For example, the artificial neural network may correspond to the artificial neural network described in FIG. 2 above.

For example, the system may receive an annotated image of a training data embryo. For example, the training data embryo may be annotated with a known morphological or morpho-kinetic feature. The system may then train the initial artificial neural network to classify images with the known morphological or morpho-kinetic feature in the first classification. Once trained, the system may receive a first image of the first embryo that is input into the initial artificial neural network. The system may then receive the first output from the initial artificial neural network indicating that the first image includes the known morphological or morpho-kinetic feature.

At step 804, process 800 (e.g., via one or more components of system 300 (FIG. 3)) receives a second output from the first artificial neural network indicating the embryo has a second classification at a second time point. In some embodiments, the system may use known implantation data in order to increase the amount of training data available. One drawback of known implantation data is that the known implantation data may be incomplete. That is, known implantation data may not indicate whether or not an embryo was viable. Additionally or alternatively, the known implantation data may have lapses in the images of the embryo during incubation, resulting in an incomplete morpho-kinetic signature.

In some embodiments, the system may preprocess incomplete data (e.g., known implantation data) through bootstrapping or other processes. For example, the system may use bootstrapping, a statistical tool, to generate an additional sample set of training data. For example, data on embryo viability and/or time-lapse images of incubating embryos may not be available. In such cases, the system may resample data from the existing data set. For example, the system may resample test data (e.g., which was previous separated from training data) to generate more training data. The system may resample the data to ensure that the resampled data is randomly and independently generated from the test data.

For example, the system may receive known implantation data indicating that the first embryo has a first classification at a first time point. The system may then generate a bootstrap label based on the known implantation data, wherein the bootstrap label corresponds to a second classification at a second time point. The system may then aggregate the first classification and the second classification to generate the first morpho-kinetic signature.

At step 806, process 800 (e.g., via one or more components of system 300 (FIG. 3)) aggregates the first output and second output to generate a morpho-kinetic signature. For example, the morpho-kinetic signature is a representation of morpho-kinetic events in the embryo as a function of time. For example, each output may correspond to a single time-lapse image. The system may determine the morpho-kinetic event in each time-lapse image and generate a vector value corresponding to the morpho-kinetic event. The system may then aggregate these values to generate the morpho-kinetic signature.

It is contemplated that the steps or descriptions of FIG. 8 may be used with any other embodiment of this disclosure. In addition, the steps and descriptions described in relation to FIG. 8 may be done in alternative orders or in parallel to further the purposes of this disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously to reduce lag or increase the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in relation to FIGS. 1 and 3-4 could be used to perform one or more of the steps in FIG. 8.

FIG. 9 shows a flowchart of the steps involved in using morpho-kinetic signatures to predict viability of embryos, in accordance with one or more embodiments. It should be noted that in addition to predicting the viability of embryos, the steps of process 900 may be used for other classifications (e.g., an implantation quality, a preimplantation genetic screening result, a likelihood of viability, or a predicted morphological feature).

At step 902, process 900 (e.g., via one or more components of system 300 (FIG. 3)) receives a plurality of outputs from a first artificial neural network based on a series of images of an embryo. For example, the system may determine a series of morpho-kinetic events that correspond to a series of time-lapse images of an embryo during incubation. The system may use an initial artificial neural network (e.g., as described in FIG. 2) to perform this function.

At step 904, process 900 (e.g., via one or more components of system 300 (FIG. 3)) generates a morpho-kinetic signature based on the plurality of outputs. For example, the morpho-kinetic signature may be a representation of morpho-kinetic events in the embryo as a function of time. In some embodiments, the system may further generate graphical representations of the morpho-kinetic signature as shown in FIGS. 5-6.

At step 906, process 900 (e.g., via one or more components of system 300 (FIG. 3)) inputs the morpho-kinetic signature into a second artificial neural network. For example, the system may have trained the second artificial neural network to classify inputted morpho-kinetic signatures. In some embodiments, the system may train the second artificial neural network on labeled morpho-kinetic signatures (e.g., morpho-kinetic signatures annotated as corresponding to a high or low viability embryo).

In some embodiments, the system may generate the first morpho-kinetic signature of the first embryo based on known implantation data and may determine a classification for the first morpho-kinetic signature through a comparison of other morpho-kinetic signatures. For example, in order to increase the amount of training data for the system, the system may use known implantation data. Known implantation may include time-lapse images of embryos (e.g., for use in generating a morpho-kinetic signature). However, the known implantation data may lack a label as to whether or not the embryo was viable. This may be particularly true as the viability of the embryo may depend on additional factors after implantation (e.g., the conditions of the uterus, etc.). Moreover, the ultimate viability may not be determinable if multiple embryos were implanted, but only a single embryo become viable.

To determine which of the embryos became viable, the system may compare the morpho-kinetic signatures. These morpho-kinetic signatures comprise an additional data point to use to determine which of the embryos became viable. For example, the system may match each of the morpho-kinetic signatures to morpho-kinetic signatures that are already labeled as viable. If one implanted embryo has a morpho-kinetic signature with a high correlation to the morpho-kinetic signatures of viable embryos and one implanted embryo does not have a high correlation to the morpho-kinetic signatures of viable embryos, the system can determine that the embryo with the high correlation was the viable embryo. This embryo (and its morpho-kinetic signature) may then be added to the data set as a morpho-kinetic signature resulting in a viable embryo. Likewise, the converse is true for the other embryo and its morpho-kinetic signature.

For example, the system may determine the known classification for the first embryo (e.g., an implantation quality, a preimplantation genetic screening result, a likelihood of viability, or a predicted morphological feature) based on a comparison of the first morpho-kinetic signature and a third morpho-kinetic signature, wherein the third morpho-kinetic signature corresponds to a third embryo that was implanted with the first embryo, and wherein the first embryo was viable and the third embryo was not viable.

For example, the system may train a model with a modified target (=loss) function. This function may use KID embryos (e.g., with known positive or negative classifications). The system may also use KID Unknown embryos (e.g., embryos with partial information such as images of their development but without a label classification such as whether or not the embryo was viable). For example, for two jointly transferred KID Unknown embryos, which ended up in a single live birth, the system will need to receive one high prediction score and one low prediction score. Using these scores, the system may distinguish between the two embryos and determine the embryo that was viable. The system may also penalize (or deemphasize data) with alternative arrangements (e.g., two low scores or two high scores) because in such cases the system cannot identify which embryo was viable. Through this additional processing step, the system may use KID Unknown embryos in training, which may lead to a better model (as it relies on higher data amounts).

At step 908, process 900 (e.g., via one or more components of system 300 (FIG. 3)) receives an output from the second artificial neural network indicating a viability of the embryo. It is contemplated that the steps or descriptions of FIG. 9 may be used with any other embodiment of this disclosure. In addition, the steps and descriptions described in relation to FIG. 9 may be done in alternative orders or in parallel to further the purposes of this disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously to reduce lag or increase the speed of the system or method. Furthermore, it should be noted that any of the devices or equipment discussed in relation to FIGS. 1 and 3-4 could be used to perform one or more of the steps in FIG. 9.

FIG. 10 shows a user interface for a system for filtering user data related to embryonic implantation, in accordance with one or more embodiments. For example, user interface 1000 may relate to a user interface of a software application (e.g., implemented on system 300 (FIG. 3)) for use in an embryonic transfer and implantation. For example, through the use of user interface 1000, users (e.g., a medical professional) may better select, navigate, and filter patient data in order to select an embryo for transfer and/or implantation, determine an optimal decision time related to the embryonic transfer and/or implantation, and/or otherwise improve the embryo for transfer and/or implantation process.

User interface 1000 includes a plurality of patient records. Each record may include individual information related to the patient. For example, column 1002 may include a user identification (e.g., a name, serial number, and/or other identifier) used to link given data to a given patient. User interface 1000 may also list clinical data such as age, weight, body mass index, endometrial thickness, as well as other medical and demographic data (e.g., family history, race, etc.) associated with each patient record (e.g., as shown in column 1004).

User interface 1000 may also include diagnosis information (e.g., in column 1006), which describes current events/issues related to a patient record and/or information on a current diagnosis related to the transfer and/or implantation. User interface 1000 may also include search bar 1008 for searching for and/or filtering patient records based on one or more criteria. Each record in user interface 1000 may be selectable. For example, selection of record 1012 may cause the system to generate additional patient information (e.g., in panel 1010). The additional patient information may provide a more detailed description of the current status and treatments for the patient related to the transfer and/or implantation.

User interface 1000 may also allow for progress filters that may indicate current patient records that have begun the preparation and/or procedures related to transfer and/or implantation. For example, selection of record 1012 may (e.g., if the process has begun the preparation and/or procedures related to transfer and/or implantation) may generate another window and/or user interface instance (e.g., as seen in FIG. 11).

FIG. 11 shows a user interface for a system for summarizing user data related to embryonic implantation, in accordance with one or more embodiments. For example, user interface 1100 may represent a window and/or user interface generated by the system for a patient record that is currently in progress. For example, user interface 1100 may represent a user interface generated by the system in response to a user selection of record 1012 (FIG. 10)).

For example, user interface 1100 may list patient data 1102. Patient data 1102 may list different treatments (and/or statistics for those treatments) undergone by a patient as well as other clinical data relevant to a treatment. For example, user interface 1100 may include a date, a total number of eggs, a number of matured eggs, an number and/or percent of fertilized eggs, a number and/or percent of qualified embryos (e.g., based on a morpho-kinetic signature), a number of transferred embryos, a number of frozen embryos, a number of discarded embryos, a number of embryos that reach blastulation, a number and/or percentage of embryos that were implanted, whether or not a pregnancy resulted (and/or the number of embryos subject to the pregnancy), and/or a number of live births for each treatment.

User interface 1100 may also include graphical representations 1104, which may graphically express patient data 1102. For example, graphical representations 1104 may provide qualitative and/or quantitative descriptions of one or more treatments such as well status and/or success rates. Treatments listed in patient data 1102 may also be selectable. For example, a user may select treatment record 1106, the system may then generate additional information (e.g., information related to an underlying morpho kinetic signature for the treatment).

FIG. 12 shows a user interface for a system for generating for display information related to a morpho-kinetic signature for use in identifying optimal decision times for embryonic implantation, in accordance with one or more embodiments. For example, user interface 1200 may represent additional information generate by the system in response to a user selection of treatment record 1106 (FIG. 11).

User interface 1200 may include a plurality of listings for embryos used in a selected treatment. User interface 1200 may additionally include thumbnails of morpho-kinetic events for each of the embryos (e.g., thumbnails 1202). Each thumbnail may include additional information related to the statistics of the embryo. It should be noted that in some embodiments, the statistics for each embryo are based on both characteristics of an embryo (e.g., its morpho-kinetic signature) as well as characteristics about a mother (e.g., age, body-mass index, family history, and/or other clinical data).

For example, each thumbnail of thumbnails 1202 may provide a snapshot of the progression of the development of the embryo (e.g., from day 1 to 5). Thumbnails 1202 may correspond to a specific well, and selection of thumbnails 1202 may cause the system to generate for display additional information about the embryo such as time-lapse video of the embryonic development (e.g., in video window 1206).

User interface 1200 may also include option 1208. Option 1208 (e.g., “Analyze Wells”) may cause the system to determine and analyze a morpho-kinetic signature for a selected embryo. In response to the analysis, the system may generate window 1204. Window 1204 may include information about each morpho-kinetic events and/or morpho-kinetic features of the embryos such as the probability that each was reached, a time each was reached, and/or the distribution when that feature is normally reached in an embryo. For example, window 1204 may include results of a deep learning model that may be trained to classify cell splits (e.g., cell splits one through nine), the development of morula, the start of blastulation, and the pronuclei appearance and fading. Additionally, window 1204 may include the results of a deep learning model trained to classify an embryo into the one or more of the Gardner expansion degrees: (1) early blastocyst (e.g., where the blastocoel formed less than half of the volume of the embryo): (2) blastocyst (e.g., where the blastocoel formed more than half of the volume of the embryo): (3) full blastocyst (e.g., where the blastocoel completely filled the embryo): (4) expanded blastocyst (e.g., where the blastocoel volume was larger than that of the early embryo, and the zona had begun to thin): (5) hatching blastocyst (e.g., where the trophectoderm had begun to herniate though the zona): and (6) hatched blastocyst (e.g., where the blastocyst had completely escaped from the zona). Each of theses morpho-kinetic features may be selectable to cause a time-lapse video (e.g., in video window 1206) of the feature to be shown.

Window 1204 may additionally or alternatively (e.g., in icon 1210) include information on determined based on the results of a deep learning model as to the probability (e.g., based on the morpho-kinetic signature) that the embryo will reach blastulation and/or a probability of success of a successful implantation.

User interface 1200 may also include analysis 1212 which may include a combination chart that displays the progression of the events combined with the predictive elements. Furthermore, an icon in analysis 1212 may track and indicate a progression based on a selection of a morpho-kinetic event and/or morpho-kinetic feature in window 1204. The movement of the progression may simultaneously modify a video displayed in video window 1206.

Each of thumbnails (or the embryo associated with the thumbnails) may be selectable to have an analysis performed. For example, in response to a selection of thumbnails 1202 and a user selection of icon 1214, the system may analyze the embryo (or the morpho-kinetic signature of the embryo) and provide an indication of its likelihood to reach blastulation, success of implantation, and/or generate a comparison to other embryos. User interface 1200 may also include an icon used to generate a transfer calculator as shown in FIG. 13.

FIG. 13 shows a user interface for a system for generating display information related to identifying optimal decision times for embryonic implantation, in accordance with one or more embodiments. It should be noted that in some embodiments, the statistics for each embryo are based on both characteristics of an embryo (e.g., its morpho-kinetic signature) as well as characteristics about a mother (e.g., age, body-mass index, family history, and/or other clinical data). For example, in some embodiments, the probability of success may also be based on a predicted thickness of the endometrium (e.g., as described in FIG. 17).

For example, user interface 1300 may include a transfer calculator as selected from user interface 1200 (FIG. 12). For example, user interface 1300 may include embryos list 1310. Embryos list 1310 may include information on individual embryos as well as a probability related to each. For example, when transferring one or more embryos (e.g., N embryos), embryos list 1310 may provide predictions for each of 0 through N possible implantation outcomes. For example, to generate embryo list 1310, the system may determine possible outcomes for each embryo (e.g., based on a morpho-kinetic signature). These individual outcomes may then be used to determine a composite outcome for a group of selected embryos.

As an illustrative example, a user (e.g., a medical professional) may wish to determine a likelihood of success based on a selection of a plurality of embryos (e.g., three embryos). With regards to three embryos, the system may determine a probability of success of the four potential outcomes (i.e., zero implantations, one implantation, two implantations, or three implantations). The system may then sum the individual probabilities for each embryo to determine a likelihood of each of the four potential outcomes. User interface 1300 may then display this information in table 1308.

User interface 1300 may also include an icon related to the number of total transfers (e.g., icon 1302), a predicted pregnancy probability (e.g., icon 1304), and/or a graphical representation of the potential outcomes (e.g., graphical representation 1306) for each selected embryo and/or group of embryos.

FIGS. 14-15 show illustrative graphs related to implantation and blastulation scores as a function of time, in accordance with one or more embodiments. In some embodiments, these scores (e.g., indicating a probability of success) may be used to populate user interfaces as shown in FIGS. 10-13 and reflect the results of a trained deep learning model. Moreover, these scores may be used to identify an optimal time for transferring an embryo. For example, a deep learning model may be used to identify embryos with an optimal time for transfer as well as identify the optimal time of the transfer.

FIG. 14 shows an illustrative graph related to an implantation score as a function of time, in accordance with one or more embodiments. For example, graph 1400 charts an implantation score (e.g., probability of successful implantation) as a function of time. In graph 1400, the y-axis represents different embryo (e.g., out of a 2000 embryo batch). The x-axis represents time (e.g., over a time of 24 to 120 hours). The coloration of a given embryo along the x-axis corresponds to its probability of successful implantation over the time period. Furthermore, the probability of success along the x-axis is based on determinations at two hour intervals. Each determination is based on the morpho-kinetic signature of a respective embryo at that time based on a model trained for the respective interval.

FIG. 15 shows an illustrative graph related to a blastulation score as a function of time, in accordance with one or more embodiments. Similar to graph 1400 (FIG. 14), graph 1500 charts a blastulation score (e.g., probability of successful blastulation blastulation) as a function of time. In graph 1500, the y-axis represents different embryo (e.g., out of a 2,000 embryo batch). The x-axis represents time (e.g., over a time of 24 to 120 hours). The coloration of a given embryo along the x-axis corresponds to its probability of successful implantation over the time period. Furthermore, the probability of success along the x-axis is based on determinations at two hour intervals. Each determination is based on the morpho-kinetic signature of a respective embryo at that time based on a model trained for the respective interval.

The morpho-kinetic signatures (or heat maps of these morpho-kinetic signatures) shown in graph 1400 (FIG. 14) and graph 1500 may be categorized into four groups. First, embryos with low probabilities (e.g., for successful blastulation or implantation, respectively) are indicated by consistent dark lines. These embryos may be selected by the system as having a low probability and discarded. Second, embryos with initial high scores, and which exhibit a sudden and/or abrupt drop may be identified. The system may compare the drop based on one or more thresholds (e.g., corresponding to the interval). The system may then discard these embryos. For example, these embryos are likely to have experienced an environmental problem based on issues with temperature control, a light problem, a medium problem, etc. In some embodiments, the system may also detect embryos belonging to this second group if other embryos experienced correlated drops (e.g., indicating issues with an entire batch). Third, the system may detect embryos with a consistently rising score over time that reaches a relatively stable score at a certain point. The system may identify these as good embryos that will likely maintain good implantation probability. The system may also detect embryos in this group if the score stabilizes on a relatively high score.

Finally, the system may identify a fourth group of embryos. The system may identify these embryos based on the embryo achieving a certain implantation score after which the score may exhibit a slow decline. In graph 1400 (FIG. 14), an example of a fourth group embryo may be found at line 1409, which is shown in graph 1600 (FIG. 16).

FIG. 16 shows an illustrative graph identifying an optimal time for transferring an embryo, in accordance with one or more embodiments. As shown in FIG. 14, this embryo has an increasing score until roughly 80 to 84 hours, after which it monotonically decreases until its removal from the incubator at 116 hours. As described in FIG. 17, the system may train a deep learning model to identify the optimal time for transfer of an embryo.

FIG. 17 shows a flowchart for a method of identifying endometrial thickness, in accordance with one or more embodiments. For example, process 1700 describes a method of predicting a future endometrial thickness based on a past endometrial thickness.

At step 1702, process 1700 receives (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) a first image of an endometrium at a first time point. For example, the image may indicate a first endometrial thickness at the first time point. The first image may comprise the entire length of the endometrium or may only include some portions of the endometrium.

At step 1704, process 1700 inputs the image into a trained artificial neural network to predict a thickness of the endometrium at a second time point. For example, the system may train (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) an artificial neural network to match the endometrial thickness in images of endometriums (e.g., taken at a later time point) with the endometrial thickness in images the endometriums (e.g., taken at an early time point). During training, each image may be labeled with an endometrial thickness that the image corresponds to. Each label may additionally include a serial number or other identifier used to denote the patient and/or endometrium. The system may train the artificial neural network to determine the endometrial thickness of an endometrium at a later time point based on characteristics of the endometrium at an earlier time point.

In some embodiments, the predicted the endometrial thickness may further be based on information from the patient. For example, the system may further use clinical data about the patient to predict an endometrial thickness. In such cases, a feature vector used to train the artificial neural network may include information related to the image (e.g., information related to the pixel values of the image) and information related to values for clinical data categories. The system may then train the artificial neural network to predict endometrial thicknesses based on early endometrial thicknesses and clinical data about the patient. For example, the system may receive an input of both a first image in step 1702 as well as clinical data about the mother. The system may then generate a feature input for use in the trained artificial neural network.

At step 1706, process 1700 receives an output from the artificial neural network indicating the thickness of the endometrium at a second time point. It should be noted that in some embodiments, the endometrium thickness may be used in conjunction with the morpho-kinetic signature to determine and optimal transfer time (e.g., as described in FIGS. 7 and 10-17 above).

It is contemplated that the steps or descriptions of FIG. 17 may be used with any other embodiment of this disclosure. In addition, the steps and descriptions described in relation to FIG. 17 may be done in alternative orders or in parallel to further the purposes of this disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously to reduce lag or increase the speed of the system or method. FIG. 18 shows a flowchart for a method of selecting embryos based on predictive positive value scores, in accordance with one or more embodiments. For example, process 1800 describes a process for selecting which embryos from a plurality of embryos to implant when the embryos have different incubation times.

At step 1802, process 1800 receives (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) a first predictive positive value score corresponding to a first time point. For example, the first predictive positive value score may correspond to a first time point during incubation (e.g., at fifty hours) of a first embryo.

At step 1804, process 1800 receives (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) a second predictive positive value score corresponding to a second time point. For example, the second predictive positive value score may correspond to a second time point during incubation (e.g., at ninety hours) of a second embryo. For example, as a result of differences in incubation times, different embryos may have different current incubation times: however, in order to ensure optimal results (e.g., as described in FIG. 13 above), the system may need to combine embryos having different current incubation times. For example, the system may determine which embryo is better (e.g., a first embryo currently at fifty hours of incubation or a second embryo currently at fifty hours of incubation).

At step 1806, process 1700 normalizes (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) the first predictive positive value score and the second predictive positive value score. For example, the system converts the first predictive positive value score and the second predictive positive value score into a probability value reflective of the probability that each will be successfully implanted. For example, the system may determine that the first embryo has an 80% success rate based on a comparison of the morpho-kinetic signature of the first embryo to other morpho-kinetic signatures of embryos at fifty hours. The system may then determine that the second embryo has an 90% success rate based on a comparison of the morpho-kinetic signature of the second embryo to other morpho-kinetic signatures of embryos at ninety hours.

At step 1808, process 1700 selects (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) an embryo based on the normalized first predictive positive value score and the normalized second predictive positive value score. For example, if the system determines that the first embryo has an 80% success rate based on a comparison of the morpho-kinetic signature of the first embryo to other morpho-kinetic signatures of embryos at fifty hours, and the system determines that the second embryo has an 90% success rate based on a comparison of the morpho-kinetic signature of the second embryo to other morpho-kinetic signatures of embryos at ninety hours, the system may select the second embryo based on the higher probability of success.

It is contemplated that the steps or descriptions of FIG. 18 may be used with any other embodiment of this disclosure. In addition, the steps and descriptions described in relation to FIG. 18 may be done in alternative orders or in parallel to further the purposes of this disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously to reduce lag or increase the speed of the system or method.

FIG. 19 shows a flowchart for a method of predicting a time of peak implantation success, in accordance with one or more embodiments. For example, process 1800 describes a process for selecting which embryos from a plurality of embryos to implant when the embryos have different incubation times.

At step 1902, process 1700 receives (e.g., using control circuitry of one or more components of system 300 (FIG. 3))

At step 1902, process 1900 receives (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) a morpho-kinetic signature of an embryo with an unknown time of peak implantation. For example, the system may receive a morpho-kinetic signature of an embryo with an unknown time of peak implantation, wherein the morpho-kinetic signature is a representation of morpho-kinetic events in the embryo as a function of time. For example, the known time of peak implantation corresponds to a time when a probability of success of implantation is highest.

In some embodiments, the system may receive a first output from an initial artificial neural network indicating that the first embryo has a first classification at a first time point, receive a second output from the initial artificial neural network indicating the first embryo has a second classification at a second time point, and aggregate the first output and second output to generate the first morpho-kinetic signature.

In some embodiments, each morpho-kinetic event of the morpho-kinetic events is represented as a float value in a vector (e.g., as described in FIGS. 3 and 5 above), and the first morpho-kinetic signature is based on a series of time-lapse images of the morpho-kinetic events in the first embryo (e.g., as described in FIG. 12 above).

At step 1904, process 1900 receives (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) the morpho-kinetic signature into an artificial neural network. For example, the system may input the morpho-kinetic signature into an artificial neural network, wherein the artificial neural network is trained to detect to a time of peak implantation for the morpho-kinetic signature based on a set of training data comprising morpho-kinetic signatures labeled with known times of peak implantation.

For example, the system may receive a first morpho-kinetic signature of a first embryo, wherein the first morpho-kinetic signature is a representation of morpho-kinetic events in the first embryo as a function of time. The system may then label the first morpho-kinetic signature with a known time of peak implantation. The system may then train the artificial neural network to detect the known time of peak implantation based on the first morpho-kinetic signature.

In some embodiments, the system may also train the artificial neural network based on endometrial thickness and clinical data. For example, the system may label the first morpho-kinetic signature with a known endometrial thickness, and system may train the artificial neural network to detect the known time of peak implantation based on the first morpho-kinetic signature and the known endometrial thickness. Additionally or alternatively, the system may label the first morpho-kinetic signature with known clinical data, and the system may train the artificial neural network to detect the known time of peak implantation based on the first morpho-kinetic signature and the known clinical data.

At step 1906, process 1900 receives (e.g., using control circuitry of one or more components of system 300 (FIG. 3)) a prediction for a time of peak implantation for the morpho-kinetic signature. For example, the system may receive a prediction (e.g., indicating a date), from the artificial neural network, for the time of peak implantation for the morpho-kinetic signature.

For example, the system may receive (e.g., in step 1902) a second morpho-kinetic signature of a second embryo with an unknown time of peak implantation, wherein the second morpho-kinetic signature is a representation of morpho-kinetic events in the second embryo as a function of time. The system may input the second morpho-kinetic signature into the trained artificial neural network. The system may then receive a prediction from the trained artificial neural network that the second morpho-kinetic signature corresponds to the known time of peak implantation.

In some embodiments, the system may determine a first probability of successful implantation for the second embryo based on the prediction, determine a second probability of successful implantation for a third embryo, and determine a composite probability of success for implanting both the second embryo and the third embryo based on the first probability and the second probability (e.g., as described in FIG. 13 above).

In some embodiments, the system may determine a first probability of successful implantation for the second embryo based on the prediction, determine a second probability of successful implantation for a third embryo, normalize the first probability and the second probability, and select the second embryo based on the normalized first probability being higher than the normalized second probability (e.g., as described in FIG. 18 above).

It is contemplated that the steps or descriptions of FIG. 19 may be used with any other embodiment of this disclosure. In addition, the steps and descriptions described in relation to FIG. 19 may be done in alternative orders or in parallel to further the purposes of this disclosure. For example, each of these steps may be performed in any order or in parallel or substantially simultaneously to reduce lag or increase the speed of the system or method.

FIG. 20 shows a block diagram 2000 for determining a predicted implantation potential of an embryo based on PGS data and morphokinetic signatures, in accordance with one or more embodiments. Related to other embodiments disclosed herein, implementations of physical and/or software systems and methods may be utilized to predict an actual implantation potential of an embryo based not only on results of PGS testing, but also on known implantation data (e.g., whether a similar embryo was successfully implanted regardless of any particular feature of the embryo). In this way, while classifying an embryo as euploid for aneuploid may be a factor in predicting its implantation potential, in contrast to prior art systems, the present system does not regard such a classification as necessarily determinative. The embodiments of FIGS. 20 and 21 illustrate algorithms for generating a recommendation for implantation based on a predicted implantation potential, including embodiments where PGS data is (FIG. 20), or is not (FIG. 21), available. With regard to the artificial neural networks and particularly the utilization of multiple artificial neural networks, many features described below may be similar to those described with reference to FIG. 4 or with regard to other technical features discussed throughout the present disclosure.

As depicted in FIG. 20, the embodiment of the algorithm shown may include the system receiving PGS data 2004 that may provide, among other things, the number of chromosomes in the embryo. The PGS data may also explicitly classify the embryo as euploid or aneuploid based on said number of chromosomes.

The PGS data 2004 may be provided to a convertor 2006 that utilizes the PGS data's classification of the embryo as (an)euploid and converts this classification to a format suitable for use in an artificial neural network, herein referred to as a first feature output 2010. For example, the convertor may generate a vector or other equivalent data structures or values that interface with the second artificial neural network 2012, as further detailed below. However, in some embodiments, the PGS data (or equivalent classification) may be directly input into the second artificial neural network 2012, i.e., such embodiments may not have convertor 2006.

The output of the converter 2006 may be provided to the second artificial neural network 2012 for use in scoring the implantation potential of the embryo. Additionally, similar to the embryo's PGS data, a “first feature input” 2002 may be input that includes morphokinetic signatures derived from the TLI video of the embryo. It is understood in the present disclosure that when reference is made to “a morphokinetic signature” that such includes not just the time-dependent aspects of an embryo but may also include one or more images during one or more particular times during the evolution of an embryo. In other portions of the disclosure, such images and the embryo features depicted therein are referred to as “morphological” aspects or signatures. Thus, in general, any discussion of morphokinetics necessarily contemplates use of any morphological features contained therein. In some implementations, the morphokinetic signatures may be based on time-lapse incubator (TLI) images or video of an embryo, the images or still frames of such may be extracted and thus depict morphological signatures or features of the embryo at given point(s) in time.

The system may input the first feature output 2010 and the first feature input 2002 into a second artificial neural network 2012 to generate a second feature output 2016 based on a predicted implantation potential of the embryo. In this way, the second artificial neural network may be configured to utilize both morphokinetic signatures of the embryo and the (an)euploid classification of the embryo, as well as additional optional inputs (e.g., age, body mass index, etc.) as described below. Thus, the second artificial neural network has improved predictive ability because whether an embryo is classified as (an)euploid, the known implantation data (and optional other factors as described below) are further utilized to generate the predicted implantation potential.

The second artificial neural network 2012 may be trained with training data 2014 to determine predicted implantation potentials of embryos based on classifications (e.g., euploid or aneuploid) of morphokinetic and/or morphological signatures and known implantation data (e.g., whether the associated embryo successfully implanted—regardless of its (an)euploid status).

Embodiments of the second artificial neural network may be trained to utilize a variety of training data. In a first implementation, the training may include, for example, training the second artificial neural network with training videos of embryos that depict training morphokinetic signatures and associated known implantation data. Accordingly, its use may include inputting a morphokinetic feature from a video that includes the morphokinetic signatures as part of determining the predicted implantation potential.

In a second implementation, the training may include, for example, training the second artificial neural network with training patient ages and associated known implantation data. Accordingly, its use may include inputting a patient age as part of determining the predicted implantation potential.

In a third implementation, the training may include, for example, training the second artificial neural network with training patient body mass indices and associated known implantation data. Accordingly, its use may include inputting a body mass index as part of determining the predicted implantation potential.

In a fourth implementation, the training may include, for example, training the second artificial neural network with training fertilization techniques used for embryos and associated known implantation data. Accordingly, its use may include inputting a fertilization technique for the embryo as part of determining the predicted implantation potential.

Other implementations may utilize other metrics for training and making a prediction, for example, the ovarian stimulation protocol (how was the egg obtained), what drugs were given to the patient, culture media (where did the embryo grow, what was the 02 level, etc.).

The second feature output may be similar to the first feature output in that may be in the form of, for example, a vector, or other usable output that reflects the predicted implantation potential as determined by the second artificial neural network. The predicted implantation potential may be any quantification that relates to the likelihood of a successful implantation by the second artificial neural network. For example, the predicted implantation potential may be a numerical score such that an ascending number may indicate an increased likelihood of successful implantation. While in some implementations the predicted implantation potential may be a predicted percent chance of success, such is not necessary and instead the predicted implantation potential may only be a more general indication as described above. Accordingly, the predicted implantation potential may be reflective of a determined confidence level in the result. Such a confidence level is not necessarily directly related to the percentage chance of the successful outcome.

The second feature output may be utilized to generate and/or provide a recommendation for implantation. Such a recommendation may be displayed, for example, as a graphical output at a display device. The graphical output may take the form of, for example, a binary result such as yes or no, a red or green icon or other graphical feature, etc.

FIG. 21 shows a block diagram for determining a predicted implantation potential of an embryo based on morphokinetic signatures, in accordance with one or more embodiments. This embodiment 2100 is similar to the embodiment 2000 of FIG. 20, but the first artificial neural network 2106 does not require PGS data as an input. This is because in this embodiment, the first artificial neural network 2106 generates inferred PGS data 2104 (e.g., aneuploid vs. euploid, chromosomal abnormalities, etc.) from morphokinetic signatures. In such embodiments, the first artificial neural network 2106 may include training data 2008 having training PGS data and/or training morphokinetic signatures. In this way, the first artificial neural network may be trained to associate particular morphokinetics with an associated (an)euploid classification. Accordingly, with this internally-generated inferred PGS data based on the 2002 first feature input, the first feature output may be generated without receiving actual PGS data as an input. This has the advantage of not requiring patients to undergo a PGS procedure to obtain the disclosed predicted implantation potential.

FIG. 22 shows a flowchart for a method 2200 of determining a predicted implantation potential of an embryo, in accordance with one or more embodiments.

At 2202, the method may include receiving a first feature input. For example, the first feature input may be based on a morphokinetic signature of an embryo. In some embodiments, the method may include obtaining a morphokinetic signature from a video of the development of the embryo.

At 2204, the method may determining a first feature output based on a classification of the embryo. For example, the classification may be as euploid or aneuploid. Some embodiments may include training the first artificial neural network with training PGS data and training morphokinetic signatures and receiving PGS data that indicates whether the embryo is euploid or aneuploid, wherein the classification is based at least on the PGS data. The PGS data may be converted into a first feature output having a format compatible for interfacing with the second artificial neural network. The first feature output may then be provided to the second artificial neural network. Yet other embodiments, such as depicted in FIG. 21, may include training the first artificial neural network with training PGS data and training morphokinetic signatures, generating inferred PGS data from at least the morphokinetic signatures, and inputting, to the first artificial network, the inferred PGS data, wherein the classification is further based at least on the inferred PGS data.

At 2206, the method may include inputting the first feature output and the first feature input into a second artificial neural network to generate a second feature output. For example, the second feature output may be based on a predicted implantation potential of the embryo. In some embodiments, the predicted implantation potential may be a numerical score. Also, 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.

Training for certain embodiments may include training the second artificial neural network with training images of embryos that depict training morphological features and associated known implantation data. Furthermore, the method may also include inputting a morphological feature from an image that includes the morphokinetic signatures as part of determining the predicted implantation potential or inputting a morphokinetic feature from a video that includes the morphokinetic signatures as part of determining the predicted implantation potential. In other embodiments, the method may include training the second artificial neural network with training patient ages and associated known implantation data and inputting a patient age as part of determining the predicted implantation potential. In yet other embodiments, the method may include training the second artificial neural network with training patient body mass indices and associated known implantation data and inputting a body mass index as part of determining the predicted implantation potential. In some embodiments, the method may include training the second artificial neural network with training fertilization techniques used for embryos and associated known implantation data and inputting a fertilization technique for the embryo as part of determining the predicted implantation potential.

At 2208, the method may include generating for display, on a user interface, a recommendation for implantation based on the second feature output.

Although the present invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but on the contrary, is intended to cover modifications and equivalent arrangements that are within the scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

The present techniques will be better understood with reference to the following enumerated embodiments:

- 1. A method of classifying morphological and morpho-kinetic features in embryos, the method comprising: receiving a first image of a first embryo: labeling the first image with a known morphological or morpho-kinetic feature in the first image: training an artificial neural network to detect the known morphological or morpho-kinetic feature in the first image; receiving a second image of a second embryo: inputting the second image into the trained artificial neural network: and receiving an output from the trained artificial neural network indicating that the second image includes the known morphological or morpho-kinetic feature.
- 2. A method of classifying morpho-kinetic signatures in embryos, the method comprising: receiving a first morpho-kinetic signature of a first embryo, wherein the first morpho-kinetic signature is a representation of morpho-kinetic events in the first embryo as a function of time; labeling the first morpho-kinetic signature with a known classification: training an artificial neural network to detect the known classification based on the first morpho-kinetic signature; receiving a second morpho-kinetic signature of a second embryo with an unknown classification, wherein the second morpho-kinetic signature is a representation of morpho-kinetic events in the second embryo as a function of time: inputting the second morpho-kinetic signature into the trained artificial neural network: and receiving a prediction from the trained artificial neural network that the second morpho-kinetic signature corresponds to the known classification.
- 3. A method of generating morpho-kinetic signatures for embryos, the method comprising: receiving a first output from a first artificial neural network indicating that an embryo has a first classification at a first time point: receiving a second output from the first artificial neural network indicating the embryo has a second classification at a second time point: and aggregating the first output and second output to generate a morpho-kinetic signature, wherein the morpho-kinetic signature is a representation of morpho-kinetic events in the embryo as a function of time.
- 4. A method of predicting viability of embryos based on morpho-kinetic signatures, the method comprising: receiving a plurality of outputs from a first artificial neural network based on a series of images of an embryo: generating a morpho-kinetic signature based on the plurality of outputs, wherein the morpho-kinetic signature is a representation of morpho-kinetic events in the embryo as a function of time: inputting the morpho-kinetic signature into a second artificial neural network: and receiving an output from the second artificial neural network indicating a viability of the embryo.
- 5. A method for determining a predicted implantation potential of an embryo, comprising: receiving a first feature input, wherein a first feature input is based on a morphokinetic signature of an embryo: determining a first feature output based on a classification of the embryo as euploid or aneuploid: inputting the first feature output into a second artificial neural network to generate a second feature output based on a predicted implantation potential of the embryo, wherein the second artificial neural network is trained to predict predicted implantation potentials of embryos based on classifications of morphokinetic signatures and known implantation data: and generating for display, on a user interface, a recommendation for implantation based on the second feature output.
- 6. The method of any of the preceding embodiments, further comprising:
- receiving PGS data that indicates whether the embryo is euploid or aneuploid, wherein the classification is further based at least on the PGS data: converting the PGS data into a first feature output having a format compatible for interfacing with the second artificial neural network: and providing the first feature output to the second artificial neural network.
- 7. The method of any of the preceding embodiments, further comprising:
- training the first artificial neural network with training PGS data and training morphokinetic signatures: generating inferred PGS data from at least the morphokinetic signatures; and inputting, to the first artificial network, the inferred PGS data, wherein the classification is further based at least on the inferred PGS data.
- 8 The method of any of the preceding embodiments, wherein the predicted implantation potential is a numerical score.
- 9. The method of any of the preceding embodiments, further comprising:
- training the second artificial neural network with training images of embryos that depict training morphological features and associated known implantation data: and inputting a morphological feature from an image that includes the morphokinetic signatures as part of determining the predicted implantation potential.
- 10. The method of any of the preceding embodiments, further comprising: training the second artificial neural network with training videos of embryos that depict training morphokinetic signatures and associated known implantation data: and inputting a morphokinetic feature from a video that includes the morphokinetic signatures as part of determining the predicted implantation potential.
- 11. The method of any of the preceding embodiments, further comprising:
- training the second artificial neural network with training patient ages and associated known implantation data: and inputting a patient age as part of determining the predicted implantation potential.
- 12. The method of any of the preceding embodiments, further comprising: training the second artificial neural network with training patient body mass indices and associated known implantation data: and inputting a body mass index as part of determining the predicted implantation potential.
- 13. The method of any of the preceding embodiments, further comprising: training the second artificial neural network with training fertilization techniques used for embryos and associated known implantation data: and inputting a fertilization technique for the embryo as part of determining the predicted implantation potential.
- 14. The method of any of the preceding embodiments, wherein the morphokinetic signature is obtained from a video of the development of the embryo.
- 15. A tangible, non-transitory, machine-readable medium storing instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform operations comprising those of any of embodiments 1-14.
- 16. A system comprising: one or more processors: and memory storing instructions that, when executed by the processors, cause the processors to effectuate operations comprising those of any of embodiments 1-14.
- 17. A system comprising means for performing any of embodiments 1-14.

METHODS AND SYSTEMS FOR EMBRYO CLASSIFICIATION

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