Ai algorithm for embryo survival

Abstract

Methods and systems for estimating a likelihood of survival of an embryo are provided. Aspects of the present invention include methods for estimating a likelihood of survival of an embryo comprising: removing an embryo from a cryopreservation subsystem, wherein the embryo is a cryopreserved embryo, introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo, obtaining images of the embryo in the embryo culture subsystem at a plurality of different times, and applying a computer-implemented model to the images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate a likelihood of survival of the embryo based on the images. Also provided are systems for performing the methods described herein as well as non-transitory computer readable storage media.

Description

[0001] Al ALGORITHM FOR EMBRYO SURVIVAL

[0002] CROSS-REFERENCE TO RELATED APPLICATION The application claims the benefit of United States Provisional Patent Application Serial No. 63 / 748,354 filed January 22, 2025, the disclosure of which application is incorporated herein by reference in its entirety.

[0003] INTRODUCTION

[0004] In vitro fertilization (IVF) is a process of fertilization in which an egg is combined with sperm in vitro (“in glass”). The process involves monitoring and stimulating a patient’s ovulatory process, then removing an ovum or ova (egg or eggs) from the patient’s ovaries. The ovum or ova are then fertilized with sperm in a culture medium in a laboratory. The fertilized egg, i.e. embryo, is ultimately transferred into the uterus.

[0005] Embryos transferred into the uterus can be either “fresh” from fertilized egg cells of the same menstrual cycle, or “frozen,” that is they have been generated in a preceding cycle and undergone embryo cryopreservation, and are thawed just prior to the transfer, which is then termed “frozen embryo transfer” (FET). Embryo cryopreservation allows for embryos to be stored such that they can be implanted at a later time, including years later.

[0006] To select embryos to be transferred into the uterus, laboratories have developed grading methods to judge oocyte and embryo quality. Such grading methods that are used to optimize pregnancy rates include morphologic grading, morphokinetics, blastocyst culture, and preimplantation genetic testing for aneuploidies. However, many current methods fail to predict the impact of possible freeze-thaw damage in frozen embryos. Greater than 95% of thawed embryos survive the at the point of initial evaluation (e.g., before they are implanted), but there remains uncertainty in correlating the thawed embryos with longer-term embryo survival and ultimately different outcomes of live-birth, pregnancy, stillbirth or miscarriage. The ability to understand the embryo thaw process is crucial for selecting competent embryos that can lead to pregnancy.SUMMARY

[0007] Thus, there is a need for improved and useful methods and systems for estimating a likelihood of survival of an embryo in connection with frozen embryo transfers. This invention provides such new and useful methods and systems, addressing the limitations mentioned above. Embodiments of the present invention relate to a novel approach involving using artificial intelligence tools for assessing or estimating or evaluating a likelihood of embryo survival in the context of in vitro fertilization including in the context of frozen embryo transfers. Embodiments of the present invention can be used, for example, to assess a likelihood that an embryo will survive a frozen embryo transfer, or can be used to select an embryo among a plurality of embryos for implantation.

[0008] This summary is meant to provide some examples and is not intended to be limiting of the scope of the invention in any way. For example, any feature included in an example of this summary is not required by the claims, unless the claims explicitly recite the features. Various features and steps as described elsewhere in this disclosure may be included in the examples summarized here, and the features and steps described here and elsewhere can be combined in a variety of ways.

[0009] Methods, systems, and non-transitory computer readable storage mediums for estimating a likelihood of survival of an embryo are provided.

[0010] Aspects of the present invention include methods for estimating a likelihood of survival of an embryo comprising: removing an embryo from a cryopreservation subsystem, wherein the embryo is a cryopreserved embryo, introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo, obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times, applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.Aspects of the present invention further include methods for estimating a likelihood of survival of an embryo comprising: obtaining a cryopreserved embryo, introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo, obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times, applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0011] Aspects of the present invention still further include methods for estimating a likelihood of survival of an embryo comprising: introducing a cryopreserved embryo into an embryo culture subsystem for controlled treatment of the embryo; obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0012] Systems and non-transitory computer-readable mediums for performing the subject methods are also provided.

[0013] The methods and systems find use in a variety of different applications, and contexts, e.g., in a healthcare-related context, such as, for example, training and utilizing artificial intelligence models to predict a likelihood of survival of an embryo in the context of in vitro fertilization treatment, including in the context of a frozen embryo transfer. The methods and systems may relate to images obtained using a variety of different imaging modalities, including, for example, images obtained using photographic imaging modalities (e.g., microscope imaging modalities), images obtained using MRI imaging modalities, images obtained using X-ray imaging modalities, images obtained using CT imaging modalities, images obtained using ultrasound imaging modalities, images obtained using ultraviolet imaging modalities, images obtained using infraredimaging modalities or imagines obtained using a combination of imaging modalities.

[0014] Other features and advantages of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings which illustrate, by way of example, the principles of the invention.

[0015] Further details regarding in vitro fertilization (IVF), including assessment of likelihoods of live birth events, can be found in, e.g., US Patent No. 9,458,495, US Patent No. 10,438,686, US Patent No. 10,717,957, as well as International Publication No. WO 2014 / 033210 A1 and International Publication No. WO 2019 / 113643, the disclosures of which are incorporated herein.

[0016] BRIEF DESCRIPTION OF THE FIGURES

[0017] The invention may be best understood from the following detailed description when read in conjunction with the accompanying drawings. Included in the drawings are the following figures:

[0018] FIGS. 1A-1C illustrate flow diagrams for estimating a likelihood of survival of an embryo according to embodiments of the present invention.

[0019] FIG. 2 depicts an image of cells and Al-predicted nuclei labelling and cell segmentation, thus highlighting aspects of embryo development of interest in connection with estimating a likelihood of survival of an embryo according to embodiments of the present invention.

[0020] FIGS. 3A-3B depict images of embryos that survived at the end of a 24 hour reference time versus images of embryos that did not survive at the end of a 24 hour reference period.

[0021] FIG. 4 depicts a functional block diagram for a computer system according to certain embodiments.

[0022] FIG. 5 depicts a general architecture of an example computing device according to certain embodiments.DETAILED DESCRIPTION

[0023] The embodiments of the invention described herein are not intended to be exhaustive or to limit the invention to precise forms disclosed. Rather, the embodiments selected for description have been chosen to enable one skilled in the art to practice the invention.

[0024] Aspects of the present invention include methods of estimating a likelihood of survival of an embryo comprising: removing an embryo from a cryopreservation subsystem, wherein the embryo is a cryopreserved embryo, introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo, obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times and applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0025] Aspects of the present invention further include methods for estimating a likelihood of survival of an embryo comprising: obtaining a cryopreserved embryo, introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo, obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times, applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0026] Aspects of the present invention still further include methods for estimating a likelihood of survival of an embryo comprising: introducing a cryopreserved embryo into an embryo culture subsystem for controlled treatment of the embryo; obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood ofsurvival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0027] Aspects of the present invention further include methods of training a model to estimate a likelihood of survival of an embryo, the method comprising: obtaining an image-based training data set, wherein the image-based training data set comprises images of one or more embryos at a plurality of different times; and training the model to detect a result using images from the imagebased training data set to estimate a likelihood of survival of the embryo at a reference time.

[0028] Also provided are systems for performing the methods described herein as well as non-transitory computer readable storage media.

[0029] Before the present invention is described in greater detail, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0030] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0031] Certain ranges are presented herein with numerical values being preceded by the term “about.” The term “about” is used herein to provide literal support for the exact number that it precedes, as well as a number that is near to or approximately the number that the term precedes. In determining whether a number is near to or approximately a specifically recited number, the near orapproximating unrecited number may be a number which, in the context in which it is presented, provides the substantial equivalent of the specifically recited number.

[0032] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative illustrative methods and materials are now described.

[0033] All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

[0034] It is noted that, as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely,” “only” and the like in connection with the recitation of claim elements, or use of a “negative” limitation.

[0035] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.While the system and method may be described for the sake of grammatical fluidity with functional explanations, it is to be expressly understood that the claims, unless expressly formulated under 35 U.S.C. § 112, are not to be construed as necessarily limited in any way by the construction of “means” or “steps” limitations, but are to be accorded the full scope of the meaning and equivalents of the definition provided by the claims under the judicial doctrine of equivalents, and in the case where the claims are expressly formulated under 35 U.S.C. § 112 are to be accorded full statutory equivalents under 35 U.S.C. § 112.

[0036] METHODS FOR ESTIMATING A LIKELIHOOD OF SURVIVAL OF AN EMBRYO

[0037] Aspects of the present invention include methods of estimating a likelihood of survival of an embryo comprising: removing an embryo from a cryopreservation subsystem, wherein the embryo is a cryopreserved embryo; introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo; obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; and applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0038] FIG. 1A illustrates a flow diagram 100 for estimating a likelihood of survival of an embryo according to embodiments of the present invention. By embryo, it is typically meant a human embryo, although embryos of other species are also contemplated in certain embodiments. Further, by embryo, it is meant an embryo that has been cryopreserved (i.e. , a frozen embryo), prior to being implanted, although non-cryopreserved embryos (i.e., fresh embryos) are also contemplated in certain embodiments. Still further, by survival of the embryo, it is meant survival of an embryo after a specified amount of time, such as a specified amount of time after an embryo is removed from a cryopreservation subsystem, or a specified amount of time after an embryo is introduced to an embryo culturesubsystem, a specified amount of time after an embryo is ready to be implanted, or a specified amount of time after an embryo is implanted. For example, in some cases, by survival of the embryo, it is meant, whether or a not an embryo has survived at one, two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or more hours after the embryo is removed from the cryopreservation subsystem and introduced into the embryo culture subsystem. In other cases, by survival of the embryo, it is meant, whether or a not an embryo has survived at one, two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or more hours after the embryo is implanted. More generally, by survival of the embryo, it is meant, whether or a not an embryo has survived a specified interval of time after a reference time, where the reference time may correspond to an event, such as removal of the embryo from a cryopreservation subsystem or introduction of the embryo to an embryo culture subsystem or implantation of the embryo, and the specified interval of time may be any amount of time of interest, such as, one, two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48 or more hours after the reference time. In some embodiments, the reference time is approximately 24 hours after the time that the embryo is introduced into the embryo culture subsystem (i.e. , the incubating start time).

[0039] In FIG. 1A, flow diagram 100 starts at step 101. At step 101 , an embryo is removed from a cryopreservation subsystem. In embodiments, the embryo is a frozen embryo.

[0040] In embodiments, by cryopreservation subsystem, it is meant any convenient system, including commercially available systems, capable of maintaining embryos in a cryopreserved state. In embodiments, cryopreservation subsystems may be configured for vitrification or slow freezing or combinations thereof, as such are known in the art. In embodiments, one ormore cryoprotective agent (CPA) or cryoprotectants may be introduced to an embryo in connection with cryopreserving the embryo. Cryopreservation subsystems of interest may comprise one or more tanks of liquid gas, such as, for example, one or more tanks of liquid nitrogen. Cryopreservation subsystems of interest may comprise a cryogenic level controller for controlling and monitoring the liquid gas, e.g., liquid nitrogen, level within a cryogenic storage vessel; a controlled rate freezer for creating a desired temperature change (i.e., freezing curve); a cryogenic storage vessel and / or a vessel inventory system. In embodiments, such liquid nitrogen tanks may be maintained at a temperature of approximately -196 degrees C. In embodiments, embryos may be cryopreserved by fully or partially submerging the embryo present in a storage vessel in liquid nitrogen or positioning the embryo present in a storage vessel proximal to liquid nitrogen (i.e., “dry” storage) Commercially available cryopreservation subsystems comprise, for example, Kryo 370, Kryo 570 & 570-ART, Kryo 750 or Kryo 1060 available from Planer Limited, or FREEZE CONTROL® systems available from CryoLogic. Commercially available cryopreservation subsystems may comprise one or more components available from Cryo Products B.V. or Cryotherm Inc. Further details regarding cryopreservation systems can be found in, e.g., US Patent No. 11,607,691 and US Patent No. 9,297,499, the disclosures of which are incorporated herein.

[0041] By removing an embryo from the cryopreservation subsystem, it is meant no longer exposing the embryo to a freezing stimulus or freezing environment, i.e., such that the embryo can be thawed and no longer cryopreserved. In embodiments, a single embryo is removed from the cryopreservation subsystem; however, it is also contemplated that more than one embryo may be removed to be thawed simultaneously.

[0042] Upon completing step 101 in FIG. 1A of flow diagram 100, the process next moves to step 102 of flow diagram 100.

[0043] At step 102, the embryo removed from the cryopreservation subsystem at step 101 is introduced into an embryo culture subsystem. An embryo can be introduced into an embryo culture subsystem by physically placing the embryointo a cryopreservation subsystem; however, in some instances, the embryo requires one or more treatments in connection with introducing the embryo into an embryo culture subsystem, such as, for example, removing one or more cryoprotective agents (CPA) or cryoprotectants from the cryopreserved embryo, rehydrating one or more aspects of the embryo or other steps required for successfully thawing a cryopreserved embryo and / or preparing a cryopreserved embryo for implantation. In embodiments, the cryopreserved embryo is thawed after its removal from the cryopreservation subsystem.

[0044] By embryo culture subsystems it is meant any convenient system, including commercially available systems, capable of incubating an embryo, i.e. , maintaining an embryo under conditions that promote survival of the embryo. In some embodiments, the embryo culture subsystem maintains the embryo under conditions that promotes the successful thawing of the embryo (e.g., the cryopreserved embryo). In some embodiments, one or more media are introduced to the embryo to make adequate nutritive substances available to the embryo. In embodiments, introducing the embryo into an embryo culture subsystem comprises exposes the embryo to a culture medium. In embodiments, the embryo culture subsystems are configured to culture the embryo, i.e., maintain the embryo under controlled conditions. In embodiments, by embryo culture subsystems are configured to promote the development, i.e., growth, of an embryo placed therein. In embodiments, the embryo culture subsystem is configured for one or more of: thawing the embryo, incubating the embryo, culturing the embryo or growing the embryo.

[0045] Embryos may remain in the embryo culture subsystem for any amount of time of interest, such as, one, two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 or more hours. In certain cases, the embryo remains in the embryo culture subsystem for approximately 2 to 6 hours. In some embodiments, the embryo remains in the embryo culture subsystem for a certain period of time for testing or modeltraining purposes. In some embodiments, the embryo remains in the embryoculture subsystem for approximately 24 hours for testing or model-training purposes. In some embodiments, the embryo remains in the embryo culture subsystem for a certain period of time for clinical applications. In some embodiments, the embryo remains in the embryo culture subsystem for approximately 1 to 24 hours (e.g., 2 hours, 2.5 hours, 3 hours, 3.5 hours, 4 hours, 4.5 hours, 5 hours, 5.5 hours, 6 hours) for clinical applications, including, e.g., 1 to 12 hours, 1 to 8 hours, 1 to 6 hours, 1.5 to 12 hours, 1.5 to 8 hours, 1.5 to 6 hours, 2 to 12 hours, 2 to 8 hours, 2 to 6 hours, 2 to 5 hours, 2 to 4 hours, 2.5 to 12 hours, 2.5 to 8 hours, 2.5 to 6 hours, 2.5 to 5 hours, 2.5 to 4 hours, 3 to 12 hours, 3 to 8 hours and 3 to 6 hours for clinical applications. In certain cases, the embryo remains in the embryo culture subsystem for approximately 2 to 6 hours for clinical applications.

[0046] In embodiments, the embryo is introduced into the embryo culture subsystem for controlled treatment of the embryo at an incubating start time, images of the embryo in the embryo culture subsystem are obtain at a plurality of different times after the incubating start time until an implantation time, and the reference time is a time that is later than the implantation time. By implantation time it is meant the time that the embryo is implanted into or is ready to be implanted (e.g., implanted into a patient’s uterus). In certain embodiments, the implantation time corresponds to a time after the incubating start time at which the embryo is ready for implantation. In some embodiments, the implantation time is approximately 1 to 24 hours (e.g., 2 hours, 2.5 hours, 3 hours, 3.5 hours, 4 hours, 4.5 hours, 5 hours, 5.5 hours, 6 hours) after the incubation start time, including, e.g., 1 to 12 hours, 1 to 8 hours, 1 to 6 hours, 1.5 to 12 hours, 1.5 to 8 hours, 1.5 to 6 hours, 2 to 12 hours, 2 to 8 hours, 2 to 6 hours, 2 to 5 hours, 2 to 4 hours, 2.5 to 12 hours, 2.5 to 8 hours, 2.5 to 6 hours, 2.5 to 5 hours, 2.5 to 4 hours, 3 to 12 hours, 3 to 8 hours and 3 to 6 hours after the incubation start time.

[0047] Embryo culture subsystems of interest comprise one or more sensors for measuring environmental conditions of the embryo, such as temperature sensors, pH sensors, O2 sensors, or the like. Commercially available embryoculture subsystems comprise, for example, CT37staxTM- Multi Chamber Benchtop Incubator available from Planer Limited.

[0048] In embodiments, the embryo culture subsystem comprises one or more imaging devices, such as cameras. Cameras of the embryo culture subsystem may be configured to obtain images of the embryo in the embryo culture subsystem at a plurality of different times, such as a plurality of time-lapse still images or movie images. Such cameras may be configured to obtain visible light images, infrared light images, ultraviolet light images, images based on other light spectra of interest or combinations thereof. In embodiments, any convenient camera, or plurality of cameras may be employed, such as commercially available consumer digital cameras. Cameras of interest are capable of generating images of various resolutions, such as, for example, images of 1 megapixel (MP) resolution or greater, such as 0.5 MP, 1 MP, 2 MP, 4 MP, 8 MP, 16 MP, 32 MP, 64 MP or greater. While it is contemplated that embodiments of the invention may utilize any number of images of different light spectra at different resolutions, in some cases, it is preferable to utilize images of visible light spectra at resolutions of about 0.5 MP or greater (e g., 0.5 MP, 0.6 MP, 0.7 MP, 0.8 MP, 0.8 MP, 1 MP, 1.5 MP, 2 MP, 2.5 MP, 4 MP, 8 MP, 16 MP, 32 MP, or 64 MP or greater). In some cases, black and white images are obtained, and in other cases, color images are obtained. In some embodiments, images are obtained using a light spectrum (e.g., illumination wavelength) of 400 nm to 800 nm. In some embodiments, the images are obtained at a light spectrum of 400 nm, 455 nm, 470 nm, 505 nm, 530 nm, 590 nm, 625 nm or 630 nm. In some embodiments, the illumination time for each obtained image is 500 ms or less (e.g., 500 ms, 400 ms, 300 ms, 200 ms, 150 ms, 100 ms, 80 ms, 60 ms, 40 ms, 20 ms, 10 ms or 5 ms) including, but not limited to, 400 ms or less, 300 ms or less, 200 ms or less, 150 ms or less, 100 ms or less, 80 ms or less, 60 ms or less, 40 ms or less, 20 ms or less, 10 ms or less or 5 ms or less.

[0049] Cameras integrated into the embryo culture subsystem may offer advantages, such as eliminating or minimizing the need to manipulate the embryo in order to obtain images or ensuring that a plurality of images obtainedover time are correspond to the same relative positions of the camera and the embryo (i.e. , such that differences between images are more likely to correspond to differences between the embryo over time and not artifacts of the placement the embryo and camera). Cameras used to obtain images of embryos need not be integrated into embryo culture subsystem, and one or more separate cameras and / or camera mountings may be employed in embodiments.

[0050] Upon completing step 102 in FIG. 1A of flow diagram 100, the process next moves to step 103 of flow diagram 100.

[0051] At step 103, a plurality of images of the embryo in the embryo culture subsystem are obtained at a plurality of different times.

[0052] As described herein, any convenient images may be obtained. Images of interest are color or black and white images; visible light images or infrared images or ultraviolet light images or images of other spectra of interest; images of the embryo from a plurality of different angles or perspectives or degrees of zoom, or various combinations thereof. In some embodiments, the images of the embryo in the embryo culture subsystem comprise visible light images. In some embodiments, the images are obtained at a light spectrum of 400 nm, 455 nm, 470 nm, 505 nm, 530 nm, 590 nm, 625 nm or 630 nm. In some embodiments, the illumination time for each obtained image is 500 ms or less (e.g., 500 ms, 400 ms, 300 ms, 200 ms, 150 ms, 100 ms, 80 ms, 60 ms, 40 ms, 20 ms, 10 ms or 5 ms) including, but not limited to, 400 ms or less, 300 ms or less, 200 ms or less, 150 ms or less, 100 ms or less, 80 ms or less, 60 ms or less, 40 ms or less, 20 ms or less, 10 ms or less or 5 ms or less. In some embodiments, the images of the embryo in the embryo culture subsystem comprise non-visible light images (e.g., ultraviolet light images or infrared light images). Images of the embryo may be greyscale images or color images. In certain embodiments, the images of the embryo are brightfield images. In certain embodiments, the images of the embryo in the embryo culture subsystem characterize opacity of the embryo.

[0053] Images of the embryo may be of any convenient resolution. In some embodiments, the images have a resolution of 0.5 MP or greater (e.g., 0.5 MP,0.6 MP, 0.7 MP, 0.8 MP, 0.8 MP, 1 MP, 1.5 MP, 2 MP, 2.5 MP, 4 MP, 8 MP, 16 MP, 32 MP, or 64 MP or greater).

[0054] As described herein, in some cases, images are obtained using one or more cameras integrated embryo culture subsystem. In other cases, images are obtained using one or more cameras that are separate from the embryo culture subsystem.

[0055] In embodiments, images are obtained using an image acquisition subsystem. Image acquisition subsystems comprise one or more imaging units for acquiring images of the embryo. Image acquisition subsystems comprise one or more illumination elements for illuminating the embryo. Any convenient illumination element may be employed, such as, for example, a light source such as a light emitting diode (LED), halogen lamp or the like. In some cases, an illumination element is positioned beneath the embryo or on an opposite side of the embryo as an in imaging unit. In some cases, an illumination element is positioned above the embryo or on the same side of the embryo as an in imaging unit. In some cases, an illumination element is configured such that an imaging unit acquires images indicative of light transmitted through the embryo. In some cases, the image acquisition subsystem is used to acquire information regarding the opacity of the embryo.

[0056] In embodiments, images of the embryo are obtained at different times. In embodiments, images are obtained at regular time intervals after the embryo is introduced into the embryo culture subsystem, such as every one, two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 60, 90, 120 or more minutes after introducing the embryo into the embryo culture subsystem. In certain embodiments, images of the embryo in the embryo culture subsystem are obtained at least every 10 minutes. In other cases, images need not be obtained at regular time intervals. Any convenient number of images may be obtained at different times, such as one, two, three, four, five, six, seven, eight, nine, ten images or more, e.g., images corresponding to different light spectra; angles or perspectives; resolutions; or degrees of magnification.Images of the embryo may be obtained at any convenient magnification including, but not limited to, a magnification of 2X or greater (e.g., 4X or greater, 5X or greater, 10X or greater, 16X or greater, 20X or greater, 40X or greater, 50X or greater or 100X or greater). In some embodiments, the images of the embryo are obtained at a magnification of 2X, 4X, 5X, 10X, 16X, 20X, 40X, 50X or 100X.

[0057] Images of the embryo may be obtained using an objective lens with any convenient numerical aperture (NA). The objective lens may have a NA in the range of 0.20 or greater (e.g., 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00, 1.10, 1.20, 1.30, 1.40, etc.).

[0058] In some embodiments, at each time interval, images are obtained at a single focal plane. In some embodiments, at each time interval, images are obtained at multiple focal planes. By multiple focal plane imaging, it is meant that multiple images are obtained at different depths or Z-distances relative to the embryo being imaged. In other words, in multiple focal plane imaging, the Z-distance relative to the embryo being imaged may change with each image while the field of view (i.e. , the X-Y dimensions) remain the same. For example, given the shallow depth of field of most objective lenses, only certain features of the three-dimensional embryo will be in focus in any single focal plane. In some embodiments, images are obtained at two or more focal planes (e.g., two focal planes, three focal planes, four focal planes, five focal planes, six focal planes, seven focal planes, eight focal planes, nine focal planes, 10 focal planes, 11 focal planes, 12 focal planes, 13 focal planes, 14 focal planes, 15 focal planes, 16 focal planes, 17 focal planes, 18 focal planes, 19 focal planes or 20 focal planes) including, e.g., three or more focal planes, five or more focal planes, 10 or more focal planes, 11 or more focal planes, 15 or more focal planes, or 20 or more focal planes.

[0059] In embodiments, a plurality of images of the embryo are obtained in order to identify changes of the embryo over time. In some embodiments, the images of the embryo visualize one or more of: components of cells of the embryo, organelles of cells of the embryo, embryo size, embryo volume, embryo shape, a number of cells of the embryo, or nucleoli of a cell of the embryo. In someembodiments, a plurality of images of the embryo are obtained in order to identify changes of the embryo over time. In some embodiments, the images of the embryo visualize changes in one or more of: components of cells of the embryo, organelles of cells of the embryo, embryo size, embryo volume, embryo shape, a number of cells of the embryo, or nucleoli of a cell of the embryo. In some embodiments, the changes of the embryo include growth of the embryo or lack thereof. Aspects of embodiments comprise identifying changes in the embryo that may not be visible to the human eye. Changes of interest include, for example, changes related to the size or shape of the embryo, the number of cells present in the embryo, the size or shape of cells present within the embryo, differentiation of aspects of the embryo, uniformity of the embryo or lack thereof, such as uniformity of cell characteristics throughout the embryo, or the like. In each case, such changes may not be visible to the human eye. Moreover, in embodiments, models may be trained to identify changes that represent precursor changes or antecedent changes or otherwise related changes that do not directly represent a named feature or characteristics or preciously identified type of change that a viable embryo undergoes, but instead, in light of the learning nature of the underlying model, may be trained to identify changes heretofore not previously identified or associated with embryo viability, due to, for example, such changes not being visible to the human eye or other diagnostic techniques or otherwise not having been identified. Such changes may be identified in connection with embodiments through use of models, as described herein, as well as through use of a plurality of images of the embryo, taken over a period of time, as described herein.

[0060] In some embodiments, the plurality of images includes morphological change information of the embryo overtime. In some embodiments, the plurality of images includes morphokinetic information of the embryo. By morphokinetic information it is meant time-specific morphological changes. Morphological changes to the embryo over time include, but are not limited to, expansion, cellularity, cohesion, blast size, zona pellucida (ZP) thickness, blastocyst area, time of initiation of re-expansion, presence of collapse or contraction, re-expansion rate, collapse rate, nucleus volume to cytoplasm volume ratio (N:C ratio) (e.g., N:C in trophectoderm cells, N:C in inner cell mass), trophectoderm-related measurements, presence of nucleoli, cellular granularity, cellular opacity and combinations thereof. In some embodiments, obtaining a plurality of images further comprises labelling the acquired plurality of images. In some embodiments, cells within the embryo may be segmented. By cell segmentation, it is meant the process of identifying and labeling individual cells in an image. For example, individual cells may be labeled and / or regions of the cell that correspond to the nucleus, cytoplasm or other organelles may be labeled. Many suitable tools for cell segmentation exist including, but not limited to, Convert.ai, CellSeg, DeepCell, Cellpose and Seggal. In some embodiments, cells within the embryo are segmented and the nucleus is labeled. A non-limiting example of cell segmentation and predictive nucleus labeling is shown in FIG. 2.

[0061] In some embodiments, obtaining a plurality of images further comprises assigning a quantitative score to the segmented image. For example, following cell segmentation and nucleus labeling, a nucleus volume to cytoplasm volume ratio (N:C ratio) may be calculated. Depending on the N:C ratios of the cells in the embryo individually or as a whole, a high / low N:C score may be assigned to the embryo, region of the embryo (e.g., inner cell mass, trophectoderm cells) or cell within the embryo. High / low N:C scores may be assigned to specific regions of the embryo.

[0062] Upon completing step 103 of FIG. 1A of flow diagram 100, the process next moves to step 104 of flow diagram 100.

[0063] At step 104, a computer-implemented model is applied to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images. In some embodiments, the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphological changes of the embryo overtime. In some embodiments, the model is configured to estimate the likelihood of survival of the embryo based at least in part onmorphokinetic information of the embryo. Morphological changes and morphokinetic information include those discussed above.

[0064] In some embodiments, a model may comprise one or more of: a statistical model, a linear model, a computational model, a tree-based model, an artificial intelligence model, a machine learning model, a deep learning model, a convolutional neural network, an artificial neural network, a deep learning network, a language model, such as a large language model (LLM), transformerbased models, models trained using at least in part, attention-based training mechanisms, as each such model and technique for training and applying it is known in the art. In embodiments, the model is a statistical model. In embodiments, the model is an artificial intelligence model. In embodiments, the model is a machine learning model. In embodiments, model is a deep learning model. In embodiments, the model is an artificial neural network. In embodiments, the model is a convolutional neural network.

[0065] In some embodiments, estimating a likelihood of survival of the embryo at a reference time comprises obtaining a quantitative measure of likelihood of survival. In some embodiments, estimating a likelihood of survival of the embryo at a reference time comprises obtaining a probability of likelihood of survival. In some embodiments, estimating a likelihood of survival of the embryo at a reference time comprises obtaining confidence intervals for the estimate of the likelihood of survival.

[0066] In some embodiments, estimating the likelihood of survival of the embryo at the reference time comprises inputting additional data to the model. Examples of additional data include, but are not limited to, morphokinetics of the embryo development (2PN- blastocyst), patient data, optionally comprising patient demographics, optionally comprising one or more of: age, infertility diagnosis, ovarian reserve, ethnicity, prior pregnancy history or oxidative stress profile of gamete source serum, a profile of oocyte or sperm, optionally comprising an artificial intelligence-based profile, insemination method (Cl vs ICSI), embryo cohort data, optionally comprising embryo development rate or fertilization rate or blastocyst rate, information specifying whether the embryo underwent invasivebiopsy, a lag time from embryo biopsy to embryo cryopreservation, ploidy status of embryo, oxidative stress of spent media from fresh culture, follicular fluid assessment, epigenetic data, proteomics of spent media from fresh culture or thawed media, parental genetic screening results, ovarian stimulation protocol (dosing, FSH only, or mixed protocol, trigger methods) culture media for embryo development, vitrification protocol, thaw protocol, sperm count, motility, morphology or DNA fragmentation, metabolomics of spent media from fresh culture or thawed media, optionally comprising metabolic profiles or signatures, pH of spent media from fresh culture or thawed media, embryo morphology grades prior to embryo freeze, comprehensive genetic testing, gene expression patterns from the fresh embryo or thawed embryo, analytic characteristics of the embryo, analytic characteristics of an embryo environment, embryo age, information about how the embryo was created, embryo freezing quality, embryo quality at time of freeze, embryo PGT results, embryo opacification or opacity, ratios of nucleus volume to cytoplasm volume (N:C ratio) of embryo cells, N:C ratios of embryo inner cell mass (ICM), N:C ratios of embryo trophectoderm (TE), N:C ratios of mural versus polar trophectoderm (TE) cells, identification of mural versus polar trophectoderm (TE), presence of nucleoli, or cellular granularity.

[0067] Also provided are methods of estimating a likelihood of survival of an embryo comprising: obtaining a cryopreserved embryo; introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo; obtaining a plurality of images of the embryo in the embryo culture subsystem at a plurality of different times; and applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0068] FIG. 1B illustrates a flow diagram 100 for estimating a likelihood of survival of an embryo according to embodiments of the present invention. By embryo, it is typically meant a human embryo, although embryos of other speciesare also contemplated in certain embodiments. Flow diagram 100 starts at step 101. At step 101, a cryopreserved embryo (i.e., frozen embryo) is obtained. Upon completing step 101 in flow diagram 100, the process next moves to step 102 of flow diagram 100. At step 102, the cryopreserved embryo is introduced into an embryo culture subsystem. The embryo may be introduced into an embryo culture subsystem according to embodiments such as those described above. Upon completing step 102 of flow diagram 100, the process next moves to step 103 of flow diagram 100. At step 103, a plurality of images of the embryo in the embryo culture subsystem are obtained corresponding to a plurality of different times. In embodiments, a plurality of images of the embryo are obtained in order to identify changes of the embryo over time. Images of the embryo may be obtained according to embodiments such as those described above. Upon completing step 103 of flow diagram 100, the process next moves to step 104 of flow diagram 100. At step 104, a computer-implemented model is applied to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images. Models may be applied according to embodiments such as those described above.

[0069] Also provided are methods of estimating a likelihood of survival of an embryo comprising: introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo; obtaining a plurality of images of the embryo in the embryo culture subsystem at a plurality of different times; and applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0070] FIG. 1C illustrates a flow diagram 100 for estimating a likelihood of survival of an embryo according to embodiments of the present invention. By embryo, it is typically meant a human embryo, although embryos of other speciesare also contemplated in certain embodiments. Flow diagram 100 starts at step 101. At step 101, an embryo is introduced into an embryo culture subsystem. In embodiments, the embryo is a cryopreserved embryo (i.e., frozen embryo). The embryo may be introduced into an embryo culture subsystem according to embodiments such as those described above. Upon completing step 101 of flow diagram 100, the process next moves to step 102 of flow diagram 100. At step 102, a plurality of images of the embryo in the embryo culture subsystem are obtained at a plurality of different times. In embodiments, a plurality of images of the embryo are obtained in order to identify changes of the embryo over time. Images of the embryo may be obtained according to embodiments such as those described above. Upon completing step 102 of flow diagram 100, the process next moves to step 103 of flow diagram 100. At step 103, a computer-implemented model is applied to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images. Models may be applied according to embodiments such as those described above.

[0071] Training the model:

[0072] Aspects of the present invention include methods of training a model to estimate a likelihood of survival of an embryo, the method comprising: obtaining an image-based training data set, wherein the image-based training data set comprises a plurality of training images of training embryos corresponding to a plurality of different times; and training the model to detect a result using the plurality of images from the image-based training data set to estimate a likelihood of survival of the embryo at a reference time. In some embodiments, the model is trained to detect a result using the plurality of images from the image-based training data set to estimate a likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0073] In embodiments, the model is trained based on training data. By training, it is meant configuring, fitting or otherwise preparing a model to make predictions,such as, for example, to estimate a likelihood of survival of an embryo. Training data includes, but is not limited to, an image-based training data set. In some embodiments, the model is trained based on training data, wherein the training data comprises: a plurality of training images of training embryos at a plurality of different times. In some embodiments, the plurality of training images are a plurality of training images of an embryo corresponding to a plurality of different times.

[0074] In embodiments, the training images of training embryos comprise any number of images such as one or more images, ten or more images, 100 or more images, 500 or more images, 1 ,000 or more images, or 10,000 or more images. In embodiments, the training embryos comprise two or more embryos (e.g., 5 embryos, 10 embryos, 20 embryos, 30 embryos, 40 embryos, 50 embryos, 60 embryos, 70 embryos, 80 embryos, 90 embryos, 100 embryos, 110 embryos, 120 embryos, 130 embryos, 140 embryos, 150 embryos, 160 embryos, 170 embryos, 180 embryos, 190 embryos, 200 embryos, 250 embryos, 300 embryos, 350 embryos, 400 embryos, 450 embryos, 500 embryos, 750 embryos, 1000 embryos, 1500 embryos or 2000 embryos), including, e.g., 5 or more embryos, 10 or more embryos, 20 or more embryos, 30 or more embryos, 40 or more embryos, 50 or more embryos, 60 or more embryos, 70 or more embryos, 80 or more embryos, 90 or more embryos, 100 or more embryos, 110 or more embryos, 120 or more embryos, 130 or more embryos, 140 or more embryos, 150 or more embryos, 160 or more embryos, 170 or more embryos, 180 or more embryos, 190 or more embryos, 200 or more embryos, 250 or more embryos, 300 or more embryos, 350 or more embryos, 400 or more embryos, 450 or more embryos, 500 or more embryos, 750 or more embryos, 1000 or more embryos, 1500 or more embryos and 2000 or more embryos.

[0075] In some embodiments, the training images comprise a plurality of images obtained prior to the reference time. In some embodiments, the training images comprise a plurality of images obtained prior to the implantation time. In embodiments, the training images comprise a plurality of images taken at regular times prior to the reference time (i.e., images taken at periodic times prior to thereference time). In some embodiments, the training images comprise a plurality of images taken at regular times prior to the implantation time. By regular times, it is meant training images are taken every one, two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 60, 90, 120 or more minutes prior to the reference time (e.g., implantation time). In certain embodiments, the training images comprise a plurality of images taken at regular intervals over 24 hours or more (e.g., 24 hours, 48 hours, 72 hours) after each training embryo is introduced into the embryo culture subsystem (e.g., after each training embryo is thawed). In certain embodiments, the training images comprise a plurality of images taken at regular intervals over 24 hours after each training embryo is thawed.

[0076] In embodiments, the training images comprise images of training embryos that survived and images of training embryos that did not survive. For example, FIGS. 3A and 3B show examples of training images of training embryos taken at regular intervals wherein the embryo survived (bottom panels in FIGS. 3A and 3B) and wherein the embryo did not survive (top panels in FIGS. 3A and 3B). In some embodiments, the training images comprise a first subset of training images that comprise labels indicating the images are embryos that survived and a second subset of training images that comprise labels indicating the images are embryos that did not survive. In embodiments, the first and second subsets may comprise any convenient percentage of the image-based training data set. For example, the first subset of images (i.e., images of embryos that survived) may comprise 0.01% or more, 0.1% or more, 1% or more, 5% or more, 10% or more, 50% or more, 75% or more of the image-based training data set. For example, the second subset of images (i.e., embryos that did not survive) may comprise 0.01% or more, 0.1% or more, 1% or more, 5% or more, 10% or more, 50% or more, 75% or more of the image-based training data set.

[0077] In some embodiments, the model is configured to estimate a likelihood of survival based at least in part on similarities and differences between images of training embryos that survived and training embryos that did not survive. In some embodiments, the model is configured to estimate a likelihood of survival basedat least in part on similarities and differences with changes over time depicted in the images of training embryos. In some embodiments, the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos that survived and training embryos that did not survive.

[0078] Models may be trained using an unsupervised learning technique, a semisupervised learning technique, a supervised learning technique, a parallel training technique, a round robin training technique, an attention-based training technique and combinations thereof, as each such technique is known in the art. In some embodiments, the model is trained using one or more of: supervised training, unsupervised training or semi-supervised training.

[0079] In some embodiments, the model is trained using unsupervised training. Unsupervised learning is a machine learning technique known in the art for training a model to, for example, identify or recognize patterns. Unsupervised learning comprises training a model where pre-assigned labels are not provided to the model with respect to data used to train the model. As a result, applying unsupervised learning to train a model entails the model itself discovering patterns among the training data.

[0080] In other embodiments, the model is trained using semi-supervised training. Semi-supervised learning is a machine learning technique known in the art for training a model to, for example, identify or recognize patterns. Semisupervised learning comprises training a model using both labeled and unlabeled training data.

[0081] In other embodiments, the model is trained using supervised training. Supervised learning is a machine learning technique known in the art for training a model to, for example, identify or recognize patterns. Supervised learning involves training a model here labels are provided to the model. For example, in the case of embodiments of the invention, images of embryos are labeled as surviving or not surviving, and these labels are provided in connection with training the model.In some embodiments, the model is trained using round robin training. By “round robin training,” it is meant that input data to the model (i.e. , data used to train the model) is divided into multiple partitions such that certain partitions are used to train the model and the remaining partitions are used to generate predictions using the trained model. Such processes may be iterated where partitions of data previously used to train the model are subsequently used to generate predictions using the trained model. Round robin training approaches may offer benefits including identifying which data sets used for training result in more accurate predictions.

[0082] In some embodiments, training the model comprises: initially applying the model to obtain initial predictions, and using at least a subset of the initial predictions to further train the model, wherein initially applying the model comprises obtaining information about the confidence of the prediction generated by the model and the subset of initial predictions used to further train the model correspond to higher confidence predictions. That is, in some embodiments, training the model comprises generating a prediction as well as an indication of the degree of confidence that the prediction is accurate. In some cases, the model itself is configured to generate such an indication of confidence in a prediction.

[0083] In some embodiments, a subset of such initial predictions may be selected for use in training a model based on some function or characteristic of these initial predictions or estimates. For example, if initial predictions comprising probabilities of the survival of the embryo are obtained, only those predictions associated with a probability of sufficient confidence (i.e., a confidence above a specified threshold) may be selected for use in training a model.

[0084] In some embodiments, the model is trained to estimate the likelihood of survival of the embryo at the reference time based at least in part on additional data. In some embodiments, applying the model to the images to estimate the likelihood of survival of the embryo at the reference time comprises inputting additional data to the model. Examples of additional data include, but are not limited to, morphokinetics of the embryo development (2PN- blastocyst), patientdata, optionally comprising patient demographics, optionally comprising one or more of: age, infertility diagnosis, ovarian reserve, ethnicity, prior pregnancy history or oxidative stress profile of gamete source serum, a profile of oocyte or sperm, optionally comprising an artificial intelligence-based profile, insemination method (Cl vs ICSI), embryo cohort data, optionally comprising embryo development rate or fertilization rate or blastocyst rate, information specifying whether the embryo underwent invasive biopsy, a lag time from embryo biopsy to embryo cryopreservation, ploidy status of embryo, oxidative stress of spent media from fresh culture, follicular fluid assessment, epigenetic data, proteomics of spent media from fresh culture or thawed media, parental genetic screening results, ovarian stimulation protocol (dosing, FSH only, or mixed protocol, trigger methods) culture media for embryo development, vitrification protocol, thaw protocol, sperm count, motility, morphology or DNA fragmentation, metabolomics of spent media from fresh culture or thawed media, optionally comprising metabolic profiles or signatures, pH of spent media from fresh culture or thawed media, embryo morphology grades prior to embryo freeze, comprehensive genetic testing, gene expression patterns from the fresh embryo or thawed embryo, analytic characteristics of the embryo, analytic characteristics of an embryo environment, embryo age, information about how the embryo was created, embryo freezing quality, embryo quality at time of freeze, embryo PGT results, embryo opacification or opacity, ratios of nucleus volume to cytoplasm volume (N:C ratio) of embryo cells, N:C ratios of embryo inner cell mass (ICM), N:C ratios of embryo trophectoderm (TE), N:C ratios of mural versus polar trophectoderm (TE) cells, identification of mural versus polar trophectoderm (TE), presence of nucleoli, or cellular granularity. Additional data may further include cell segmentation and labeling (e.g., subcellular labeling such as nuclei labeling) data. Training images of training embryos may be segmented and / or labeled. For example, individual cells within the embryo may be labeled and / or regions of the cell that correspond to the nucleus, cytoplasm or other organelles may be labeled. Many suitable tools for cell segmentation exist including, but not limited to, Convert.ai, CellSeg, DeepCell, Cellpose and Seggal. In some embodiments,cells within the embryo are segmented and the nucleus is labeled. A quantitative score may be assigned to the segmented image of the training data. For example, following cell segmentation and nucleus labeling, a nucleus volume to cytoplasm volume ratio (N:C ratio) may be calculated. Depending on the N:C ratios of the cells in the embryo individually or as a whole, a high / low N:C score may be assigned to the embryo, region of the embryo (e.g., inner cell mass, trophectoderm cells) or cell within the embryo. High / low N:C scores may be assigned to specific regions of the embryo.

[0085] Computer Implemented Embodiments

[0086] The various method and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system applying a method according to the present disclosure. The described functionality can be implemented in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosure.

[0087] The various illustrative steps, components, and computing systems (such as devices, databases, interfaces, and engines) described in connection with the embodiments disclosed herein can be implemented or performed by a machine, such as a general purpose processor, a graphics processor unit, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor can be a microprocessor, but in the alternative, the processor can be a controller, microcontroller, or state machine, combinations of the same, or thelike. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor can also include primarily analog components. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a graphics processor unit, a mainframe computer, a digital signal processor, a portable computing device, a personal organizer, a device controller, and a computational engine within an appliance, to name a few.

[0088] The steps of a method, process, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module, engine, and associated databases can reside in memory resources such as in RAM memory, FRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of non-transitory computer-readable storage medium, media, or physical computer storage known in the art. An external storage medium can be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0089] As described in detail herein, embodiments of the present invention relate to computer-implemented methods for estimating a likelihood of survival of an embryo. Aspects of the present invention include methods for estimating a likelihood of survival of an embryo, the method comprising: obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; and applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at areference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0090] Aspects of the present invention further include methods of training a model to estimate a likelihood of survival of an embryo, the method comprising: obtaining an image-based training data set, wherein the image-based training data set comprises a plurality of training images of training embryos at a plurality of different times; and training the model to detect a result using images from the image-based training data set to estimate a likelihood of survival of the embryo at a reference time.

[0091] SYSTEMS FOR ESTIMATING A LIKELIHOOD OF SURVIVAL OF A EMBRYO

[0092] As summarized herein, aspects of the present disclosure include systems for estimating a likelihood of survival of an embryo. Systems according to certain embodiments comprise a processor comprising memory operably coupled to the processor, wherein the memory comprises instructions stored thereon, which, when executed by the processor, cause the processor to execute steps corresponding to the subject methods described herein.

[0093] In an embodiment, a system for estimating a likelihood of survival of an embryo comprises: a processor comprising memory operably coupled to the processor, wherein the memory comprises instructions stored thereon, which, when executed by the processor, cause the processor to: obtain a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; and apply a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0094] In an embodiment, a system for estimating a likelihood of survival of an embryo comprises: a processor comprising memory operably coupled to the processor, wherein the memory comprises instructions stored thereon, which,when executed by the processor, cause the processor to: remove an embryo from a cryopreservation subsystem, wherein the embryo is a cryopreserved embryo; introduce the embryo into an embryo culture subsystem for controlled treatment of the embryo; obtain a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; and apply a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0095] In embodiments, the system is an embryo culture subsystem.

[0096] In embodiments, the system further comprises a camera configured to collect a plurality of images of the embryo. In some embodiments, the system further comprises a plurality of cameras configured to collect a plurality of images of the embryo from a plurality of different perspectives or angles. Suitable cameras configured to collect a plurality of images of the embryo include those discussed above.

[0097] Some embodiments may further comprise a display device, e.g., for displaying results of, or related to, an enhanced image-based training data set for deep learning models or the like or for displaying results of, or related to, training a deep learning model to detect a result using an enhanced training data set. Any convenient display device, such as a liquid crystal display (LCD), lightemitting diode (LED) display, plasma (PDP) display, quantum dot (QLED) display or cathode ray tube display device. The processor and / or memory may be operably connected to the display device, for example, via a wired, such as a Universal Serial Bus (USB) connection, or wireless connection, such as a Bluetooth connection.

[0098] In some instances, the systems further include one or more computers for complete automation or partial automation of the methods described herein. In some embodiments, systems include a computer having a computer readable storage medium with a computer program stored thereon.In embodiments, the system includes an input module, a processing module and an output module. The subject systems may include both hardware and software components, where the hardware components may take the form of one or more platforms, e.g., in the form of servers, such that the functional elements, i.e., those elements of the system that carry out specific tasks (such as managing input and output of information, processing information, etc.) of the system may be carried out by the execution of software applications on and across the one or more computer platforms represented of the system.

[0099] Systems may include a display and operator input device. Operator input devices may, for example, be a keyboard, mouse, or the like. The processing module includes a processor which has access to a memory having instructions stored thereon for performing the steps of the subject methods. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, memory storage devices, and input-output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or it may be one of other processors that are or will become available. The processor executes the operating system and the operating system interfaces with firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs that may be written in a variety of programming languages, such as Java, Perl, C++, other high level or low level languages, as well as combinations thereof, as is known in the art. The operating system, typically in cooperation with the processor, coordinates and executes functions of the other components of the computer. The operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, processors include analog electronics. In some embodiments, the processor includes analog electronics which provide feedback control, such as for example negative feedback control.The system memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic medium such as a resident hard disk or tape, an optical medium such as a read and write compact disc, flash memory devices, or other memory storage device. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, a tape drive, a removable hard disk drive, or a diskette drive. Such types of memory storage devices typically read from, and / or write to, a program storage medium (not shown) such as, respectively, a compact disk, magnetic tape, removable hard disk, or floppy diskette. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store a computer software program and / or data. Computer software programs, also called computer control logic, typically are stored in system memory and / or the program storage device used in conjunction with the memory storage device.

[0100] In some embodiments, a computer program product is described comprising a computer usable medium having control logic (computer software program, including program code) stored therein. The control logic, when executed by the processor the computer, causes the processor to perform functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementation of the hardware state machine so as to perform the functions described herein will be apparent to those skilled in the relevant arts.

[0101] Memory may be any suitable device in which the processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including magnetic or optical disks or tape or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer readable medium carrying necessary program code. Programming can be provided remotely to processor through a communication channel, or previously saved in a computer program product such as memory or some other portable or fixed computer readablestorage medium using any of those devices in connection with memory. For example, a magnetic or optical disk may carry the programming, and can be read by a disk writer / reader. Systems of the invention also include programming, e.g., in the form of computer program products, algorithms for use in practicing the methods as described above. Programming according to the present invention can be recorded on computer readable media, e.g., any medium that can be read and accessed directly by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage medium, and magnetic tape; optical storage media such as CD-ROM; electrical storage media such as RAM and ROM; portable flash drive; and hybrids of these categories such as magnetic / optical storage media.

[0102] The processor may also have access to a communication channel to communicate with a user at a remote location. By remote location is meant the user is not directly in contact with the system and relays input information to an input manager from an external device, such as a computer connected to a Wide Area Network (“WAN”), telephone network, satellite network, or any other suitable communication channel, including a mobile telephone (i.e. , smartphone).

[0103] In some embodiments, systems according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface can be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., Radio-Frequency Identification (RFID), Zigbee communication protocols, WiFi, infrared, wireless Universal Serial Bus (USB), Ultra Wide Band (UWB), Bluetooth® communication protocols, and cellular communication, such as code division multiple access (CDMA) or Global System for Mobile communications (GSM).

[0104] In one embodiment, the communication interface is configured to include one or more communication ports, e.g., physical ports or interfaces such as a USB port, an RS-232 port, or any other suitable electrical connection port to allow data communication between the subject systems and other externaldevices such as a computer terminal (for example, at a physician’s office or in hospital environment) that is configured for similar complementary data communication.

[0105] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the subject systems to communicate with other devices such as computer terminals and / or networks, communication enabled mobile telephones, personal digital assistants, or any other communication devices which the user may use in conjunction with other devices.

[0106] In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing Internet Protocol (IP) through a cell phone network, Short Message Service (SMS), wireless connection to a personal computer (PC) on a Local Area Network (LAN) which is connected to the internet, or WiFi connection to the internet at a WiFi hotspot.

[0107] In one embodiment, the subject systems are configured to wirelessly communicate with a server device via the communication interface, e.g., using a common standard such as 802.11 or Bluetooth® RF protocol, or an IrDA infrared protocol. The server device may be another portable device, such as a smart phone, Personal Digital Assistant (PDA) or notebook computer; or a larger device such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), as well as an input device, such as buttons, a keyboard, mouse or touch-screen.

[0108] In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in the subject systems, e.g., in an optional data storage unit, with a network or server device using one or more of the communication protocols and / or mechanisms described herein.

[0109] Output controllers may include controllers for any of a variety of known display devices for presenting information to a user, whether a human or a machine, whether local or remote. If one of the display devices provides visual information, this information typically may be logically and / or physically organizedas an array of picture elements. A graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing graphical input and output interfaces between the system and a user, and for processing user inputs. The functional elements of the computer may communicate with each other via system bus. Some of these communications may be accomplished in alternative embodiments using network or other types of remote communications. The output manager may also provide information generated by the processing module to a user at a remote location, e.g., over the Internet, phone or satellite network, in accordance with known techniques. The presentation of data by the output manager may be implemented in accordance with a variety of known techniques. As some examples, data may include SQL, HTML or XML documents, email or other files, or data in other forms. The data may include Internet URL addresses so that a user may retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject systems may be any type of known computer platform or a type to be developed in the future, and they may be of a class of computer commonly referred to as servers. However, they may also be a mainframe computer, a workstation, or other computer type, such as a laptop computer. They may be connected via any known or future type of cabling or other communication system including wireless systems, either networked or otherwise. They may be co-located or they may be physically separated.

[0110] Various operating systems may be employed on any of the computer platforms, possibly depending on the type and / or make of computer platform chosen.

[0111] Appropriate operating systems include Windows, iOS, Oracle Solaris, Linux, IBM i, Unix, and others.

[0112] FIG. 4 shows a functional block diagram for one example of a computer system 400 for practicing methods of the present invention, i.e. , a processor operably connected to memory, 402, for estimating a likelihood of survival of an embryo. A processor and memory 402 can be configured to implement a variety of processes to obtain a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of times and / or applying a computer-implemented model to the images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0113] An apparatus, 412 can be configured to acquire training data, such as an image-based training data set. For example, apparatus 412 may be a camera (e.g., a camera integrated into the embryo culture subsystem), and processor 402 may be operably connected to such remote camera to acquire images of the embryo in the embryo culture subsystem at a plurality of different times. A data communication channel can be included between the apparatus 412 and the processor 402. An image-based training data set can be provided to the processor 402 via the data communication channel.

[0114] The processor 402 can be configured to provide a graphical display, such as one or more images of the image-based training data set to a display device 406. For example, processor and memory 402 can be configured to cause a display device 406 to display one or more diagrams or graphs or probability distributions or images or image excerpts related to results of estimating a likelihood of survival of an embryo.

[0115] The display device 406 can be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present graphical interfaces.

[0116] The processor and memory 402 can be configured to receive adjustments to configuration settings from a first input device. The first input device can be implemented as a mouse 410, or the first device can be implemented as the keyboard 408 or other means for providing an input signal to the processor 402 such as a touchscreen, a stylus, an optical detector, or a voice recognition system. Some input devices can include multiple inputting functions. In such implementations, the inputting functions can each be considered an input device. For example, mouse 410 can include a right mouse button and a left mouse button, each of which can generate a triggering event. Such triggering event can cause the processor 402 to alter the manner in which the data is displayed or inwhich data is utilized, which portions of the data is actually displayed on the display device 406, and / or provide input to further processing such as selection of additional training data or evaluation data.

[0117] The processor 402 can be connected to a storage device 404. The storage device 404 can be configured to receive and store image-based training data or synthetic images from the processor 402. The storage device 404 can be further configured to allow retrieval of training and / or evaluation data, such as subject images or synthetic images, by the processor 402.

[0118] The display device 406 can be further configured to alter the information presented according to input received from the processor 402 in conjunction with input from the apparatus 412, the storage device 404, the keyboard 408, and / or the mouse 410.

[0119] In some implementations the processor and memory 402 can generate a user interface for use with estimating the likelihood of survival of an embryo. For example, the user interface can include a control for applying certain training data or certain evaluation data to a model.

[0120] FIG. 5 depicts a general architecture of an example computing device 500 according to certain embodiments. The general architecture of the computing device 500 includes an arrangement of computer hardware and software components. The computing device 500 may include many more (or fewer) elements than those shown in FIG. 5. It is not necessary, however, that all of these generally conventional elements be shown in order to provide an enabling disclosure. As illustrated, the computing device 500 includes a processing unit 510, a network interface 520, a computer readable medium 530, an input / output device interface 540, a display 550, and an input device 560, all of which may communicate with one another by way of a communication bus. The network interface 520 may provide connectivity to one or more networks or computing systems. The processing unit 510 may thus receive information and instructions from other computing systems or services via a network. The processing unit 510 may also communicate to and from memory 570 and / or a data store 590 and further provide output information for an optional display 550 via the input / outputdevice interface 540. The input / output device interface 540 may also accept input from the optional input device 560, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0121] The memory 570 may contain computer program instructions (grouped as modules or components in some embodiments) that the processing unit 510 executes in order to implement one or more embodiments. The memory 570 generally includes RAM, ROM and / or other persistent, auxiliary or non-transitory computer-readable media. The memory 570 may store an operating system 572 that provides computer program instructions for use by the processing unit 510 in the general administration and operation of the computing device 500. The memory 570 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0122] For example, in one embodiment, the memory 570 includes an obtain a plurality of images of the embryo corresponding to a plurality of different times module 574 and an apply a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time module 576, wherein the model is configured to estimate the likelihood of survival of the embryo at least in part on changes in the embryo over time depicted in the plurality of images.

[0123] Aspects of the present disclosure further include non-transitory computer readable storage media for generating an enhanced image-based training data set for deep learning models. Non-transitory computer readable storage media according to certain embodiments comprise one or more algorithms corresponding to the subject methods described herein.

[0124] UTILITY

[0125] The subject methods and systems find use in a variety of applications where it is desirable to estimate a likelihood of survival of an embryo. The subject methods and systems may be used in applications such as thoseinvolving previously frozen embryos (e.g., cryopreserved embryos). In some embodiments, the methods and systems described herein find use in clinical settings such as any clinical setting where traditional estimation of a likelihood of survival of an embryo according to prevailing standards of care (e.g., estimation by an embryologist) may be applied. Embodiments of the methods and systems described herein find use in clinical settings in which a plurality of images, such as photographic images or microscope images may be used in connection with estimating the likelihood of survival of an embryo. In other embodiments, the methods and systems described herein find use in remote medicine settings, where specialized medical services capable of providing accurate estimations of the likelihood of survival of an embryo may be newly enabled by application of the present methods and systems. In addition, the subject methods and systems find use in improving the assessment of embryo viability (e.g., cryopreserved embryo viability after thawing). Embodiments of methods and systems of the invention find use in addressing concerns around the impact of freeze-thaw damage and improving the selection of embryos for implantation.

[0126] The below items disclose various aspects of the invention. Each of the aspects described below can be combined with other aspects and embodiments disclosed elsewhere herein, including the claims, where the combinations are clearly compatible. Certain aspects include:

[0127] Aspect 1. A method for estimating a likelihood of survival of an embryo, the method comprising:

[0128] removing an embryo from a cryopreservation subsystem, wherein the embryo is a cryopreserved embryo;

[0129] introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo;

[0130] obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; and

[0131] applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein themodel is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0132] Aspect 2. A method for estimating a likelihood of survival of an embryo, the method comprising:

[0133] obtaining a cryopreserved embryo;

[0134] introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo;

[0135] obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; and

[0136] applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0137] Aspect 3. A method for estimating a likelihood of survival of an embryo, the method comprising:

[0138] introducing a cryopreserved embryo into an embryo culture subsystem for controlled treatment of the embryo;

[0139] obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times;

[0140] applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

[0141] Aspect 4. The method of any of the previous Aspects, wherein the plurality of images comprise morphological change information of the embryo over time.Aspect 5. The method of any of the previous Aspects, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphological changes of the embryo over time.

[0142] Aspect 6. The method of any of the previous Aspects, wherein the plurality of images comprise morphokinetic information of the embryo.

[0143] Aspect 7. The method of any of the previous Aspects, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphokinetic information of the embryo.

[0144] Aspect 8. The method of any of the previous Aspects, wherein the embryo culture subsystem is configured for one or more of:

[0145] thawing the embryo,

[0146] incubating the embryo,

[0147] culturing the embryo, or

[0148] growing the embryo.

[0149] Aspect 9. The method of any of the previous Aspects, further comprising:

[0150] thawing the cryopreserved embryo.

[0151] Aspect 10. The method of any of the previous Aspects, wherein introducing the embryo into an embryo culture subsystem comprises exposing the embryo to a culture medium.

[0152] Aspect 11. The method of any of the previous Aspects, wherein the embryo remains in the embryo culture subsystem for approximately 24 hours for testing or model-training purposes.

[0153] Aspect 12. The method of any one of Aspects 1 to 10, wherein the embryo remains in the embryo culture subsystem for approximately two to six hours for clinical applications.

[0154] Aspect 13. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem are obtained at regular intervals of time.Aspect 14. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem are obtained at least every 10 minutes.

[0155] Aspect 15. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem are obtained from one or more of: a plurality of different perspectives, a plurality of different angles, a plurality of different magnifications, a plurality of different resolutions or a plurality of different light spectra.

[0156] Aspect 16. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem comprise visible light images.

[0157] Aspect 17. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem comprise non-visible light images.

[0158] Aspect 18. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem comprise one or more of: greyscale images or color images.

[0159] Aspect 19. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem characterize opacity of the embryo.

[0160] Aspect 20. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem characterize changes in opacity of the embryo over time.

[0161] Aspect 21. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem visualize one or more of:

[0162] components of cells of the embryo,

[0163] organelles of cells of the embryo,

[0164] embryo size,

[0165] embryo volume,

[0166] embryo shape,

[0167] a number of cells of the embryo,

[0168] nucleoli of cells of the embryo.Aspect 22. The method of any of the previous Aspects, wherein images of the embryo in the embryo culture subsystem visualize changes over time in one or more of:

[0169] components of cells of the embryo,

[0170] organelles of cells of the embryo,

[0171] embryo size,

[0172] embryo volume,

[0173] embryo shape,

[0174] a number of cells of the embryo,

[0175] nucleoli of cells of the embryo.

[0176] Aspect 23. The method of any of the previous Aspects, wherein: the embryo is introduced into the embryo culture subsystem for controlled treatment of the embryo at an incubating start time,

[0177] images of the embryo in the embryo culture subsystem are obtained at a plurality of different times after the incubating start time until an implantation time, and

[0178] the reference time is a time that is later than the implantation time.

[0179] Aspect 24. The method of Aspect 23, wherein the implantation time is approximately two to six hours after the incubating start time.

[0180] Aspect 25. The method of any of Aspects 23 or 24, wherein the implantation time corresponds to a time after the incubating start time at which the embryo is ready for implantation.

[0181] Aspect 26. The method of any of Aspects 23 to 25, wherein the reference time is approximately 24 hours after the incubating start time.

[0182] Aspect 27. The method of any of the previous Aspects, wherein the model is one or more of:

[0183] a statistical model,

[0184] an artificial intelligence model,

[0185] a machine learning model,

[0186] a deep learning model,

[0187] an artificial neural network, ora convolutional neural network.

[0188] Aspect 28. The method of any of the previous Aspects, wherein the model is trained based on training data, wherein the training data comprises: a plurality of training images of training embryos.

[0189] Aspect 29. The method of Aspect 28, wherein the training images comprise a plurality of images obtained prior to the reference time.

[0190] Aspect 30. The method of Aspects 28 or 29, wherein the training images comprise a plurality of images obtained prior to the implantation time of the training embryos.

[0191] Aspect 31. The method of any of Aspects 28 to 30, wherein the training images comprise a plurality of images of each training embryo corresponding to a plurality of different times.

[0192] Aspect 32. The method of any of Aspects 28 to 31 , wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with images of training embryos that survived and training embryos that did not survive.

[0193] Aspect 33. The method of any of Aspects 28 to 32, wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos.

[0194] Aspect 34. The method of Aspect 33, wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos that survived and training embryos that did not survive.

[0195] Aspect 35. The method of any of Aspects 28 to 34, wherein the training images comprise a plurality of images taken at regular intervals prior to the reference time.

[0196] Aspect 36. The method of any of Aspects 28 to 35, wherein the training images comprise a plurality of images taken at regular times prior to the implantation time.Aspect 37. The method of any of Aspects 28 to 36, wherein the training images comprise a plurality of images taken at regular intervals over 24 hours after each training embryo is thawed.

[0197] Aspect 38. The method of any of Aspects 28 to 37, wherein the training images comprise images of training embryos that survived and images of training embryos that did not survive.

[0198] Aspect 39. The method of any of the previous Aspects, wherein the model is trained using one or more of: unsupervised training, semi-supervised training or supervised training.

[0199] Aspect 40. The method of any of the previous Aspects, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining a quantitative measure of likelihood of survival.

[0200] Aspect 41. The method of any of the previous Aspects, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining a probability of likelihood of survival.

[0201] Aspect 42. The method of any of the previous Aspects, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining confidence intervals for the estimate of the likelihood of survival.

[0202] Aspect 43. The method of any of the previous Aspects, wherein the model is trained to estimate the likelihood of survival of the embryo at the reference time based at least in part on additional data.

[0203] Aspect 44. The method of any of the previous Aspects, wherein applying the model to the images to estimate the likelihood of survival of the embryo at the reference time comprises inputting additional data to the model.

[0204] Aspect 45. The method of Aspects 43 or 44, wherein the additional data comprises one or more of the following:

[0205] morphokinetics of the embryo development (2PN- blastocyst), patient data, optionally comprising patient demographics, optionally comprising one or more of: age, infertility diagnosis, ovarian reserve, ethnicity, prior pregnancy history or oxidative stress profile of gamete source serum,a profile of oocyte or sperm, optionally comprising an artificial intelligencebased profile,

[0206] insemination method (Cl vs ICSI),

[0207] embryo cohort data, optionally comprising embryo development rate or fertilization rate or blastocyst rate,

[0208] information specifying whether the embryo underwent invasive biopsy, a lag time from embryo biopsy to embryo cryopreservation,

[0209] ploidy status of embryo,

[0210] oxidative stress of spent media from fresh culture,

[0211] follicular fluid assessment,

[0212] epigenetic data,

[0213] proteomics of spent media from fresh culture or thawed media, parental genetic screening results,

[0214] ovarian stimulation protocol (dosing, FSH only, or mixed protocol, trigger methods)

[0215] culture media for embryo development,

[0216] vitrification protocol,

[0217] thaw protocol,

[0218] sperm count, motility, morphology or DNA fragmentation, metabolomics of spent media from fresh culture or thawed media, optionally comprising metabolic profiles or signatures,

[0219] pH of spent media from fresh culture or thawed media,

[0220] embryo morphology grades prior to embryo freeze,

[0221] comprehensive genetic testing, gene expression patterns from the fresh embryo or thawed embryo,

[0222] analytic characteristics of the embryo,

[0223] analytic characteristics of an embryo environment,

[0224] embryo age,

[0225] information about how the embryo was created,

[0226] embryo freezing quality,

[0227] embryo quality at time of freeze,embryo PGT results,

[0228] embryo opacification or opacity,

[0229] ratios of nucleus volume to cytoplasm volume (N:C ratio) of embryo cells, N:C ratios of embryo inner cell mass (ICM),

[0230] N:C ratios of embryo trophectoderm (TE),

[0231] N:C ratios of mural versus polar trophectoderm (TE) cells, identification of mural versus polar trophectoderm (TE),

[0232] presence of nucleoli, or

[0233] cellular granularity.

[0234] Aspect 46. The method of any of the previous Aspects, wherein the method is a computer-implemented method.

[0235] Aspect 47. The method of any of the previous Aspects, wherein the embryo is a previously frozen embryo.

[0236] Aspect 48. The method of any of the previous Aspects, wherein the embryo is a human embryo.

[0237] Aspect 49. A method of training a model to estimate a likelihood of survival of an embryo, the method comprising:

[0238] obtaining an image-based training data set, wherein the image-based training data set comprises a plurality of training images of training embryos corresponding to a plurality of different times; and

[0239] training the model to detect a result using the plurality of images from the image-based training data set to estimate a likelihood of survival of the embryo at a reference time.

[0240] Aspect 50. A computer-implemented method for estimating a likelihood of survival of an embryo, the method comprising:

[0241] obtaining a plurality of images of the embryo in an embryo culture subsystem corresponding to a plurality of different times; and

[0242] applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based atleast in part on changes in the embryo over time depicted in the plurality of images.

[0243] Aspect 51. A system for estimating a likelihood of survival of an embryo, the system comprising:

[0244] a processor comprising memory operably coupled to the processor, wherein the memory comprises instructions stored thereon, which, when executed by the processor, cause the processor to:

[0245] obtain a plurality of images of the embryo in an embryo culture subsystem corresponding to a plurality of different times; and

[0246] apply a computer-implemented model to the plurality of images to estimate the likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate a likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality images.

[0247] Aspect 52. The system of any of Aspect 51 , further comprising:

[0248] a camera configured to collect a plurality of images of the embryo.

[0249] Aspect 53. The system of Aspects 51 or 52, further comprising:

[0250] a plurality of cameras configured to collect a plurality of images of the embryo from one or more of: a plurality of different perspectives, a plurality of different angles, a plurality of different magnifications, a plurality of different resolutions or a plurality of different light spectra.

[0251] Aspect 54. A non-transitory computer readable storage medium comprising instructions stored thereon, the instructions comprising:

[0252] algorithm for obtaining a plurality of images of the embryo in an embryo culture subsystem corresponding to a plurality of different times; and algorithm for applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.Aspect 55. The non-transitory computer readable storage medium according to Aspect 54, wherein the plurality of images comprise morphological change information of the embryo overtime.

[0253] Aspect 56. The non-transitory computer readable storage medium according to Aspects 54 or 55, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphological changes of the embryo over time.

[0254] Aspect 57. The non-transitory computer readable storage medium according to any one of Aspects 54 to 56, wherein the plurality of images comprise morphokinetic information of the embryo.

[0255] Aspect 58. The non-transitory computer readable storage medium according to any one of Aspects 54 to 57, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphokinetic information of the embryo.

[0256] Aspect 59. The non-transitory computer readable storage medium according to any one of Aspects 54 to 58, wherein the embryo culture subsystem is configured for one or more of:

[0257] thawing the embryo,

[0258] incubating the embryo,

[0259] culturing the embryo, or

[0260] growing the embryo.

[0261] Aspect 60. The non-transitory computer readable storage medium according to any one of Aspects 54 to 59, wherein the embryo remains in the embryo culture subsystem for approximately 24 hours for testing or modeltraining purposes.

[0262] Aspect 61. The non-transitory computer readable storage medium according to any one of Aspects 54 to 59, wherein the embryo remains in the embryo culture subsystem for approximately two to six hours for clinical applications.Aspect 62. The non-transitory computer readable storage medium according to any one of Aspects 54 to 61 , wherein images of the embryo in the embryo culture subsystem are obtained at regular intervals of time.

[0263] Aspect 63. The non-transitory computer readable storage medium according to any one of Aspects 54 to 62, wherein images of the embryo in the embryo culture subsystem are obtained at least every 10 minutes.

[0264] Aspect 64. The non-transitory computer readable storage medium according to any one of Aspects 54 to 63, wherein images of the embryo in the embryo culture subsystem are obtained from one or more of: a plurality of different perspectives, a plurality of different angles, a plurality of different magnifications, a plurality of different resolutions or a plurality of different light spectra.

[0265] Aspect 65. The non-transitory computer readable storage medium according to any one of Aspects 54 to 64, wherein images of the embryo in the embryo culture subsystem comprise visible light images.

[0266] Aspect 66. The non-transitory computer readable storage medium according to any one of Aspects 54 to 65, wherein images of the embryo in the embryo culture subsystem comprise non-visible light images.

[0267] Aspect 67. The non-transitory computer readable storage medium according to any one of Aspects 54 to 66, wherein images of the embryo in the embryo culture subsystem comprise one or more of: greyscale images or color images.

[0268] Aspect 68. The non-transitory computer readable storage medium according to any one of Aspects 54 to 67, wherein images of the embryo in the embryo culture subsystem characterize opacity of the embryo.

[0269] Aspect 69. The non-transitory computer readable storage medium according to any one of Aspects 54 to 68, wherein images of the embryo in the embryo culture subsystem characterize changes in opacity of the embryo over time.Aspect 70. The non-transitory computer readable storage medium according to any one of Aspects 54 to 69, wherein images of the embryo in the embryo culture subsystem visualize one or more of:

[0270] components of cells of the embryo,

[0271] organelles of cells of the embryo,

[0272] embryo size,

[0273] embryo volume,

[0274] embryo shape,

[0275] a number of cells of the embryo,

[0276] nucleoli of cells of the embryo.

[0277] Aspect 71. The non-transitory computer readable storage medium according to any one of Aspects 54 to 70, wherein images of the embryo in the embryo culture subsystem visualize changes over time in one or more of:

[0278] components of cells of the embryo,

[0279] organelles of cells of the embryo,

[0280] embryo size,

[0281] embryo volume,

[0282] embryo shape,

[0283] a number of cells of the embryo,

[0284] nucleoli of cells of the embryo.

[0285] Aspect 72. The non-transitory computer readable storage medium according to any one of Aspects 54 to 71 , wherein:

[0286] images of the embryo in the embryo culture subsystem are obtained at a plurality of different times after an incubating start time until an implantation time, and

[0287] the reference time is a time that is later than the implantation time.

[0288] Aspect 73. The non-transitory computer readable storage medium according to Aspect 72, wherein the implantation time is approximately two to six hours after the incubating start time.Aspect 74. The non-transitory computer readable storage medium according to Aspects 72 or 73, wherein the implantation time corresponds to a time after the incubating start time at which the embryo is ready for implantation.

[0289] Aspect 75. The non-transitory computer readable storage medium according to any one of Aspects 72 to 74, wherein the reference time is approximately 24 hours after the incubating start time.

[0290] Aspect 76. The non-transitory computer readable storage medium according to any one of Aspects 547566, wherein the model is one or more of:

[0291] a statistical model,

[0292] an artificial intelligence model,

[0293] a machine learning model,

[0294] a deep learning model,

[0295] an artificial neural network, or

[0296] a convolutional neural network.

[0297] Aspect 77. The non-transitory computer readable storage medium according to any one of Aspects 54 to 76, wherein the model is trained based on training data, wherein the training data comprises: a plurality of training images of training embryos.

[0298] Aspect 78. The non-transitory computer readable storage medium according to Aspect 77, wherein the training images comprise a plurality of images obtained prior to the reference time.

[0299] Aspect 79. The non-transitory computer readable storage medium according to Aspects 77 or 78, wherein the training images comprise a plurality of images obtained prior to the implantation time of the training embryos.

[0300] Aspect 80. The non-transitory computer readable storage medium according to any of Aspects 77 to 79, wherein the training images comprise a plurality of images of each training embryo corresponding to a plurality of different times.

[0301] Aspect 81. The non-transitory computer readable storage medium according to any one of Aspects 77 to 80, wherein the model is configured to estimate the likelihood of survival based at least in part on similarities anddifferences with images of training embryos that survived and training embryos that did not survive.

[0302] Aspect 82. The non-transitory computer readable storage medium according to any one of Aspects 77 to 81 , wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos.

[0303] Aspect 83. The non-transitory computer readable storage medium according to Aspect 82, wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos that survived and training embryos that did not survive.

[0304] Aspect 84. The non-transitory computer readable storage medium according to any one of Aspects 77 to 83, wherein the training images comprise a plurality of images taken at regular intervals prior to the reference time.

[0305] Aspect 85. The non-transitory computer readable storage medium according to any one of Aspects 77 to 84, wherein the training images comprise a plurality of images taken at regular times prior to the implantation time.

[0306] Aspect 86. The non-transitory computer readable storage medium according to any one of Aspects 77 to 85, wherein the training images comprise a plurality of images taken at regular intervals over 24 hours after each training embryo is thawed.

[0307] Aspect 87. The non-transitory computer readable storage medium according to any one of Aspects 77 to 86, wherein the training images comprise images of training embryos that survived and images of training embryos that did not survive.

[0308] Aspect 88. The non-transitory computer readable storage medium according to any one of Aspects 54 to 87, wherein the model is trained using one or more of: unsupervised training, semi-supervised training or supervised training.

[0309] Aspect 89. The non-transitory computer readable storage medium according to any one of Aspects 54 to 88, wherein estimating a likelihood ofsurvival of the embryo at a reference time comprises obtaining a quantitative measure of likelihood of survival.

[0310] Aspect 90. The non-transitory computer readable storage medium according to any one of Aspects 54 to 89, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining a probability of likelihood of survival.

[0311] Aspect 91. The non-transitory computer readable storage medium according to any one of Aspects 54 to 90, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining confidence intervals for the estimate of the likelihood of survival.

[0312] Aspect 92. The non-transitory computer readable storage medium according to any one of Aspects 54 to 91 , wherein the model is trained to estimate the likelihood of survival of the embryo at the reference time based at least in part on additional data.

[0313] Aspect 93. The non-transitory computer readable storage medium according to any one of Aspects 54 to 92, wherein applying the model to the images to estimate the likelihood of survival of the embryo at the reference time comprises inputting additional data to the model.

[0314] Aspect 94. The non-transitory computer readable storage medium according to Aspects 92 or 93, wherein the additional data comprises one or more of the following:

[0315] morphokinetics of the embryo development (2PN- blastocyst), patient data, optionally comprising patient demographics, optionally comprising one or more of: age, infertility diagnosis, ovarian reserve, ethnicity, prior pregnancy history or oxidative stress profile of gamete source serum, a profile of oocyte or sperm, optionally comprising an artificial intelligencebased profile,

[0316] insemination method (Cl vs ICSI),

[0317] embryo cohort data, optionally comprising embryo development rate or fertilization rate or blastocyst rate,

[0318] information specifying whether the embryo underwent invasive biopsy,a lag time from embryo biopsy to embryo cryopreservation,

[0319] ploidy status of embryo,

[0320] oxidative stress of spent media from fresh culture,

[0321] follicular fluid assessment,

[0322] epigenetic data,

[0323] proteomics of spent media from fresh culture or thawed media, parental genetic screening results,

[0324] ovarian stimulation protocol (dosing, FSH only, or mixed protocol, trigger methods)

[0325] culture media for embryo development,

[0326] vitrification protocol,

[0327] thaw protocol,

[0328] sperm count, motility, morphology or DNA fragmentation, metabolomics of spent media from fresh culture or thawed media, optionally comprising metabolic profiles or signatures,

[0329] pH of spent media from fresh culture or thawed media,

[0330] embryo morphology grades prior to embryo freeze,

[0331] comprehensive genetic testing, gene expression patterns from the fresh embryo or thawed embryo,

[0332] analytic characteristics of the embryo,

[0333] analytic characteristics of an embryo environment,

[0334] embryo age,

[0335] information about how the embryo was created,

[0336] embryo freezing quality,

[0337] embryo quality at time of freeze,

[0338] embryo PGT results,

[0339] embryo opacification or opacity,

[0340] ratios of nucleus volume to cytoplasm volume (N:C ratio) of embryo cells, N:C ratios of embryo inner cell mass (ICM),

[0341] N:C ratios of embryo trophectoderm (TE),

[0342] N:C ratios of mural versus polar trophectoderm (TE) cells,identification of mural versus polar trophectoderm (TE), presence of nucleoli, or

[0343] cellular granularity.

[0344] Aspect 95. The non-transitory computer readable storage medium according to any one of Aspects 54 to 94, wherein the embryo is a previously frozen embryo.

[0345] Aspect 96. The non-transitory computer readable storage medium according to any one of Aspects 54 to 95, wherein the embryo is a human embryo.

[0346] Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it is readily apparent to those of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

[0347] Accordingly, the preceding merely illustrates the principles of the invention. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to furthering the art and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.The scope of the present invention, therefore, is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of present invention is embodied by the appended claims. In the claims, 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) is expressly defined as being invoked for a limitation in the claim only when the exact phrase “means for” or the exact phrase “step for” is recited at the beginning of such limitation in the claim; if such exact phrase is not used in a limitation in the claim, then 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) is not invoked.

Claims

What is claimed is:

1. A method for estimating a likelihood of survival of an embryo, the method comprising:removing an embryo from a cryopreservation subsystem, wherein the embryo is a cryopreserved embryo;introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo;obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; andapplying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

2. A method for estimating a likelihood of survival of an embryo, the method comprising:obtaining a cryopreserved embryo;introducing the embryo into an embryo culture subsystem for controlled treatment of the embryo;obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times; andapplying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

3. A method for estimating a likelihood of survival of an embryo, the method comprising:introducing a cryopreserved embryo into an embryo culture subsystem for controlled treatment of the embryo;obtaining a plurality of images of the embryo in the embryo culture subsystem corresponding to a plurality of different times;applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

4. The method of any of the previous claims, wherein the plurality of images comprise morphological change information of the embryo overtime.

5. The method of any of the previous claims, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphological changes of the embryo over time.

6. The method of any of the previous claims, wherein the plurality of images comprise morphokinetic information of the embryo.

7. The method of any of the previous claims, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphokinetic information of the embryo.

8. The method of any of the previous claims, wherein the embryo culture subsystem is configured for one or more of:thawing the embryo,incubating the embryo,culturing the embryo, orgrowing the embryo.

9. The method of any of the previous claims, further comprising: thawing the cryopreserved embryo.

10. The method of any of the previous claims, wherein introducing the embryo into an embryo culture subsystem comprises exposing the embryo to a culture medium.

11. The method of any of the previous claims, wherein the embryo remains in the embryo culture subsystem for approximately 24 hours for testing or modeltraining purposes.

12. The method of any one of claims 1 to 10, wherein the embryo remains in the embryo culture subsystem for approximately two to six hours for clinical applications.

13. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem are obtained at regular intervals of time.

14. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem are obtained at least every 10 minutes.

15. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem are obtained from one or more of: a plurality of different perspectives, a plurality of different angles, a plurality of different magnifications, a plurality of different resolutions or a plurality of different light spectra.

16. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem comprise visible light images.

17. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem comprise non-visible light images.

18. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem comprise one or more of: greyscale images or color images.

19. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem characterize opacity of the embryo.

20. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem characterize changes in opacity of the embryo over time.

21. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem visualize one or more of:components of cells of the embryo,organelles of cells of the embryo,embryo size,embryo volume,embryo shape,a number of cells of the embryo,nucleoli of cells of the embryo.

22. The method of any of the previous claims, wherein images of the embryo in the embryo culture subsystem visualize changes over time in one or more of:components of cells of the embryo,organelles of cells of the embryo,embryo size,embryo volume,embryo shape,a number of cells of the embryo,nucleoli of cells of the embryo.

23. The method of any of the previous claims, wherein:the embryo is introduced into the embryo culture subsystem for controlled treatment of the embryo at an incubating start time,images of the embryo in the embryo culture subsystem are obtained at a plurality of different times after the incubating start time until an implantation time, andthe reference time is a time that is later than the implantation time.

24. The method of claim 23, wherein the implantation time is approximately two to six hours after the incubating start time.

25. The method of any of claims 23 or 24, wherein the implantation time corresponds to a time after the incubating start time at which the embryo is ready for implantation.

26. The method of any of claims 23 to 25, wherein the reference time is approximately 24 hours after the incubating start time.

27. The method of any of the previous claims, wherein the model is one or more of:a statistical model,an artificial intelligence model,a machine learning model,a deep learning model,an artificial neural network, ora convolutional neural network.

28. The method of any of the previous claims, wherein the model is trained based on training data, wherein the training data comprises: a plurality of training images of training embryos.

29. The method of claim 28, wherein the training images comprise a plurality of images obtained prior to the reference time.

30. The method of claims 28 or 29, wherein the training images comprise a plurality of images obtained prior to the implantation time of the training embryos.

31. The method of any of claims 28 to 30, wherein the training images comprise a plurality of images of each training embryo corresponding to a plurality of different times.

32. The method of any of claims 28 to 31 , wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with images of training embryos that survived and training embryos that did not survive.

33. The method of any of claims 28 to 32, wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos.

34. The method of claim 33, wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos that survived and training embryos that did not survive.

35. The method of any of claims 28 to 34, wherein the training images comprise a plurality of images taken at regular intervals prior to the reference time.

36. The method of any of claims 28 to 35, wherein the training images comprise a plurality of images taken at regular times prior to the implantation time.

37. The method of any of claims 28 to 36, wherein the training images comprise a plurality of images taken at regular intervals over 24 hours after each training embryo is thawed.

38. The method of any of claims 28 to 37, wherein the training images comprise images of training embryos that survived and images of training embryos that did not survive.

39. The method of any of the previous claims, wherein the model is trained using one or more of: unsupervised training, semi-supervised training or supervised training.

40. The method of any of the previous claims, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining a quantitative measure of likelihood of survival.

41. The method of any of the previous claims, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining a probability of likelihood of survival.

42. The method of any of the previous claims, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining confidence intervals for the estimate of the likelihood of survival.

43. The method of any of the previous claims, wherein the model is trained to estimate the likelihood of survival of the embryo at the reference time based at least in part on additional data.

44. The method of any of the previous claims, wherein applying the model to the images to estimate the likelihood of survival of the embryo at the reference time comprises inputting additional data to the model.

45. The method of claims 43 or 44, wherein the additional data comprises one or more of the following:morphokinetics of the embryo development (2PN- blastocyst), patient data, optionally comprising patient demographics, optionally comprising one or more of: age, infertility diagnosis, ovarian reserve, ethnicity, prior pregnancy history or oxidative stress profile of gamete source serum, a profile of oocyte or sperm, optionally comprising an artificial intelligencebased profile,insemination method (Cl vs ICSI),embryo cohort data, optionally comprising embryo development rate or fertilization rate or blastocyst rate,information specifying whether the embryo underwent invasive biopsy, a lag time from embryo biopsy to embryo cryopreservation,ploidy status of embryo,oxidative stress of spent media from fresh culture,follicular fluid assessment,epigenetic data,proteomics of spent media from fresh culture or thawed media, parental genetic screening results,ovarian stimulation protocol (dosing, FSH only, or mixed protocol, trigger methods)culture media for embryo development,vitrification protocol,thaw protocol,sperm count, motility, morphology or DNA fragmentation, metabolomics of spent media from fresh culture or thawed media, optionally comprising metabolic profiles or signatures,pH of spent media from fresh culture or thawed media,embryo morphology grades prior to embryo freeze,comprehensive genetic testing, gene expression patterns from the fresh embryo or thawed embryo,analytic characteristics of the embryo,analytic characteristics of an embryo environment,embryo age,information about how the embryo was created,embryo freezing quality,embryo quality at time of freeze,embryo PGT results,embryo opacification or opacity,ratios of nucleus volume to cytoplasm volume (N:C ratio) of embryo cells, N:C ratios of embryo inner cell mass (ICM),N:C ratios of embryo trophectoderm (TE),N:C ratios of mural versus polar trophectoderm (TE) cells, identification of mural versus polar trophectoderm (TE),presence of nucleoli, orcellular granularity.

46. The method of any of the previous claims, wherein the method is a computer-implemented method.

47. The method of any of the previous claims, wherein the embryo is a previously frozen embryo.

48. The method of any of the previous claims, wherein the embryo is a human embryo.

49. A method of training a model to estimate a likelihood of survival of an embryo, the method comprising:obtaining an image-based training data set, wherein the image-based training data set comprises a plurality of training images of training embryos corresponding to a plurality of different times; andtraining the model to detect a result using the plurality of images from the image-based training data set to estimate a likelihood of survival of the embryo at a reference time.

50. A computer-implemented method for estimating a likelihood of survival of an embryo, the method comprising:obtaining a plurality of images of the embryo in an embryo culture subsystem corresponding to a plurality of different times; andapplying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

51. A system for estimating a likelihood of survival of an embryo, the system comprising:a processor comprising memory operably coupled to the processor, wherein the memory comprises instructions stored thereon, which, when executed by the processor, cause the processor to:obtain a plurality of images of the embryo in an embryo culture subsystem corresponding to a plurality of different times; andapply a computer-implemented model to the plurality of images to estimate the likelihood of survival of the embryo at a reference time, wherein themodel is configured to estimate a likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality images.

52. The system of any of claim 51 , further comprising:a camera configured to collect a plurality of images of the embryo.

53. The system of claims 51 or 52, further comprising:a plurality of cameras configured to collect a plurality of images of the embryo from one or more of: a plurality of different perspectives, a plurality of different angles, a plurality of different magnifications, a plurality of different resolutions or a plurality of different light spectra.

54. A non-transitory computer readable storage medium comprising instructions stored thereon, the instructions comprising:algorithm for obtaining a plurality of images of the embryo in an embryo culture subsystem corresponding to a plurality of different times; and algorithm for applying a computer-implemented model to the plurality of images to estimate a likelihood of survival of the embryo at a reference time, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on changes in the embryo over time depicted in the plurality of images.

55. The non-transitory computer readable storage medium according to claim 54, wherein the plurality of images comprise morphological change information of the embryo over time.

56. The non-transitory computer readable storage medium according to claims 54 or 55, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphological changes of the embryo over time.

57. The non-transitory computer readable storage medium according to any one of claims 54 to 56, wherein the plurality of images comprise morphokinetic information of the embryo.

58. The non-transitory computer readable storage medium according to any one of claims 54 to 57, wherein the model is configured to estimate the likelihood of survival of the embryo based at least in part on morphokinetic information of the embryo.

59. The non-transitory computer readable storage medium according to any one of claims 54 to 58, wherein the embryo culture subsystem is configured for one or more of:thawing the embryo,incubating the embryo,culturing the embryo, orgrowing the embryo.

60. The non-transitory computer readable storage medium according to any one of claims 54 to 59, wherein the embryo remains in the embryo culture subsystem for approximately 24 hours for testing or model-training purposes.

61. The non-transitory computer readable storage medium according to any one of claims 54 to 59, wherein the embryo remains in the embryo culture subsystem for approximately two to six hours for clinical applications.

62. The non-transitory computer readable storage medium according to any one of claims 54 to 61 , wherein images of the embryo in the embryo culture subsystem are obtained at regular intervals of time.

63. The non-transitory computer readable storage medium according to any one of claims 54 to 62, wherein images of the embryo in the embryo culture subsystem are obtained at least every 10 minutes.

64. The non-transitory computer readable storage medium according to any one of claims 54 to 63, wherein images of the embryo in the embryo culture subsystem are obtained from one or more of: a plurality of different perspectives, a plurality of different angles, a plurality of different magnifications, a plurality of different resolutions or a plurality of different light spectra.

65. The non-transitory computer readable storage medium according to any one of claims 54 to 64, wherein images of the embryo in the embryo culture subsystem comprise visible light images.

66. The non-transitory computer readable storage medium according to any one of claims 54 to 65, wherein images of the embryo in the embryo culture subsystem comprise non-visible light images.

67. The non-transitory computer readable storage medium according to any one of claims 54 to 66, wherein images of the embryo in the embryo culture subsystem comprise one or more of: greyscale images or color images.

68. The non-transitory computer readable storage medium according to any one of claims 54 to 67, wherein images of the embryo in the embryo culture subsystem characterize opacity of the embryo.

69. The non-transitory computer readable storage medium according to any one of claims 54 to 68, wherein images of the embryo in the embryo culture subsystem characterize changes in opacity of the embryo over time.

70. The non-transitory computer readable storage medium according to any one of claims 54 to 69, wherein images of the embryo in the embryo culture subsystem visualize one or more of:components of cells of the embryo,organelles of cells of the embryo,embryo size,embryo volume,embryo shape,a number of cells of the embryo,nucleoli of cells of the embryo.

71. The non-transitory computer readable storage medium according to any one of claims 54 to 70, wherein images of the embryo in the embryo culture subsystem visualize changes over time in one or more of:components of cells of the embryo,organelles of cells of the embryo,embryo size,embryo volume,embryo shape,a number of cells of the embryo,nucleoli of cells of the embryo.

72. The non-transitory computer readable storage medium according to any one of claims 54 to 71 , wherein:images of the embryo in the embryo culture subsystem are obtained at a plurality of different times after an incubating start time until an implantation time, andthe reference time is a time that is later than the implantation time.

73. The non-transitory computer readable storage medium according to claim 72, wherein the implantation time is approximately two to six hours after the incubating start time.

74. The non-transitory computer readable storage medium according to claims 72 or 73, wherein the implantation time corresponds to a time after the incubating start time at which the embryo is ready for implantation.

75. The non-transitory computer readable storage medium according to any one of claims 72 to 74, wherein the reference time is approximately 24 hours after the incubating start time.

76. The non-transitory computer readable storage medium according to any one of claims 547566, wherein the model is one or more of:a statistical model,an artificial intelligence model,a machine learning model,a deep learning model,an artificial neural network, ora convolutional neural network.

77. The non-transitory computer readable storage medium according to any one of claims 54 to 76, wherein the model is trained based on training data, wherein the training data comprises: a plurality of training images of training embryos.

78. The non-transitory computer readable storage medium according to claim 77, wherein the training images comprise a plurality of images obtained prior to the reference time.

79. The non-transitory computer readable storage medium according to claims 77 or 78, wherein the training images comprise a plurality of images obtained prior to the implantation time of the training embryos.

80. The non-transitory computer readable storage medium according to any of claims 77 to 79, wherein the training images comprise a plurality of images of each training embryo corresponding to a plurality of different times.

81. The non-transitory computer readable storage medium according to any one of claims 77 to 80, wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with images of training embryos that survived and training embryos that did not survive.

82. The non-transitory computer readable storage medium according to any one of claims 77 to 81 , wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos.

83. The non-transitory computer readable storage medium according to claim 82, wherein the model is configured to estimate the likelihood of survival based at least in part on similarities and differences with changes over time depicted in the images of training embryos that survived and training embryos that did not survive.

84. The non-transitory computer readable storage medium according to any one of claims 77 to 83, wherein the training images comprise a plurality of images taken at regular intervals prior to the reference time.

85. The non-transitory computer readable storage medium according to any one of claims 77 to 84, wherein the training images comprise a plurality of images taken at regular times prior to the implantation time.

86. The non-transitory computer readable storage medium according to any one of claims 77 to 85, wherein the training images comprise a plurality of images taken at regular intervals over 24 hours after each training embryo is thawed.

87. The non-transitory computer readable storage medium according to any one of claims 77 to 86, wherein the training images comprise images of training embryos that survived and images of training embryos that did not survive.

88. The non-transitory computer readable storage medium according to any one of claims 54 to 87, wherein the model is trained using one or more of: unsupervised training, semi-supervised training or supervised training.

89. The non-transitory computer readable storage medium according to any one of claims 54 to 88, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining a quantitative measure of likelihood of survival.

90. The non-transitory computer readable storage medium according to any one of claims 54 to 89, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining a probability of likelihood of survival.

91. The non-transitory computer readable storage medium according to any one of claims 54 to 90, wherein estimating a likelihood of survival of the embryo at a reference time comprises obtaining confidence intervals for the estimate of the likelihood of survival.

92. The non-transitory computer readable storage medium according to any one of claims 54 to 91 , wherein the model is trained to estimate the likelihood ofsurvival of the embryo at the reference time based at least in part on additional data.

93. The non-transitory computer readable storage medium according to any one of claims 54 to 92, wherein applying the model to the images to estimate the likelihood of survival of the embryo at the reference time comprises inputting additional data to the model.

94. The non-transitory computer readable storage medium according to claims 92 or 93, wherein the additional data comprises one or more of the following:morphokinetics of the embryo development (2PN- blastocyst), patient data, optionally comprising patient demographics, optionally comprising one or more of: age, infertility diagnosis, ovarian reserve, ethnicity, prior pregnancy history or oxidative stress profile of gamete source serum, a profile of oocyte or sperm, optionally comprising an artificial intelligencebased profile,insemination method (Cl vs ICSI),embryo cohort data, optionally comprising embryo development rate or fertilization rate or blastocyst rate,information specifying whether the embryo underwent invasive biopsy, a lag time from embryo biopsy to embryo cryopreservation,ploidy status of embryo,oxidative stress of spent media from fresh culture,follicular fluid assessment,epigenetic data,proteomics of spent media from fresh culture or thawed media, parental genetic screening results,ovarian stimulation protocol (dosing, FSH only, or mixed protocol, trigger methods)culture media for embryo development,vitrification protocol,thaw protocol,sperm count, motility, morphology or DNA fragmentation, metabolomics of spent media from fresh culture or thawed media, optionally comprising metabolic profiles or signatures,pH of spent media from fresh culture or thawed media,embryo morphology grades prior to embryo freeze,comprehensive genetic testing, gene expression patterns from the fresh embryo or thawed embryo,analytic characteristics of the embryo,analytic characteristics of an embryo environment,embryo age,information about how the embryo was created,embryo freezing quality,embryo quality at time of freeze,embryo PGT results,embryo opacification or opacity,ratios of nucleus volume to cytoplasm volume (N:C ratio) of embryo cells, N:C ratios of embryo inner cell mass (ICM),N:C ratios of embryo trophectoderm (TE),N:C ratios of mural versus polar trophectoderm (TE) cells, identification of mural versus polar trophectoderm (TE),presence of nucleoli, orcellular granularity.

95. The non-transitory computer readable storage medium according to any one of claims 54 to 94, wherein the embryo is a previously frozen embryo.

96. The non-transitory computer readable storage medium according to any one of claims 54 to 95, wherein the embryo is a human embryo.

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