Abstract: A method for automated, artificial-intelligence-based COC retrieval includes using an artificial intelligence/machine learning system (AI/ML system) to optically scan a follicular fluid sample to produce an image object using an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes comparing the image object to a predetermined threshold using an AI/ML system, wherein the predetermined threshold is at least in part an optical pattern with a probability of corresponding to a cumulus-oocyte-complex (COC). The method includes identifying a COC within the follicular fluid sample based at least in part on the image object satisfying the predetermined threshold. The method includes determining a COC location within the follicular fluid sample based at least in part on identifying a region of the image object corresponding to an optical pattern within the image object that satisfies the predetermined threshold.

Abstract: A method includes capturing multiple images over time of objects in a sperm sample and using image processing to identify a set of candidate spermatozoa from the objects. The image processing includes identifying the set of candidate spermatozoa based on movement characteristics of the set of candidate spermatozoa observed over time. The method includes, for each respective spermatozoon of the set of candidate spermatozoa, determining a quality metric for the respective spermatozoon based on trajectory characteristics of the respective spermatozoon indicative of motility. The method includes ranking the set of candidate spermatozoa based on the quality metric. The method includes selecting a highest-ranked spermatozoon from the ranking. The method includes displaying at least a portion of the sperm sample on a screen with a visual indicator identifying the selected spermatozoon. The method includes designating the selected spermatozoon for extraction from the sperm sample and attempted fertilization.

Abstract: A method for automated, artificial-intelligence-based egg identification using optical coherence tomography (OCT) includes positioning an OCT imaging system head in proximity to a biological sample containing an oocyte, wherein the OCT is operatively coupled to an artificial intelligence/machine learning system (AI/ML system) and an imaging system, wherein the imaging system includes a camera system, and a lighting system. The method includes creating at least one three-dimensional image of the oocyte using the OCT, AI/ML system, and imaging system. The method includes using the AI/ML system to analyze the three-dimensional image, wherein an analysis includes detection of a polar body's presence or absence based at least in part on planar views of the oocyte.

Abstract: Disclosed is a method of artificial intelligence-based robotic pipetting for cell preparation. The method includes training an artificial intelligence/machine learning system (AI/ML system) to classify images of biological specimens. The method includes storing the classified images. The method includes receiving an image object from an imaging system that includes a microscopy system, a camera system configured to receive imaging from the microscopy system, and a lighting system configured to illuminate a biological material. The method includes processing the image object, using the AJ/ML system to determine a presence of a retrievable target cell. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target cell. The method includes confirming the robotic pipettor's location at the target physical orientation, using AI/ML system.

Abstract: A method of artificial-intelligence-based robotic vitrification includes placing at least one oocyte in a buffer or CPA solution. The method includes processing an image object produced by an imaging system, using an AI/ML system, to determine a location of a retrievable target oocyte. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target oocyte. The method includes confirming the robotic pipettor's location at the target physical orientation, using the AI/ML system. The method includes instructing the robotic pipettor to initiate contact with the retrievable target oocyte and apply negative pressure to secure the oocyte to the robotic pipettor. The method includes using a robotic microtool holder to lower a vitrification assembly or cryo-device of any other make into a buffer or CPA solution dish to a position in proximity to the target physical orientation.

Abstract: A method for automated, artificial-intelligence-based IVF microtool control includes receiving a command at a controller associated with an artificial intelligence/machine learning system (AI/ML system) and an imaging system used at least in part to operate robotic components of in vitro fertilization (IVF) module. The method includes retrieving at least one IVF microtool assembly (MA) from a tool inventory location using a first robotic mechanism of the robotics system, based at least in part on the command. The method includes retrieving at least one IVF receptacle using a second robotic mechanism of the robotics system based at least in part on the command. The method includes placing the IVF receptacle on a stage within the IVF module based on the command. The method includes positioning the at least one MA in proximity to the IVF receptacle, using the first robotic mechanism, based at least in part on the command.

Abstract: A method for automated, artificial-intelligence-based COC retrieval includes using an artificial intelligence/machine learning system (AI/ML system) to optically scan a follicular fluid sample to produce an image object using an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes comparing the image object to a predetermined threshold using an AI/ML system, wherein the predetermined threshold is at least in part an optical pattern with a probability of corresponding to a cumulus-oocyte-complex (COC). The method includes identifying a COC within the follicular fluid sample based at least in part on the image object satisfying the predetermined threshold. The method includes determining a COC location within the follicular fluid sample based at least in part on identifying a region of the image object corresponding to an optical pattern within the image object that satisfies the predetermined threshold.

Abstract: Disclosed is a method of artificial intelligence-based robotic pipetting for cell preparation. The method includes training an artificial intelligence/machine learning system (AI/ML system) to classify images of biological specimens. The method includes storing the classified images. The method includes receiving an image object from an imaging system that includes a microscopy system, a camera system configured to receive imaging from the microscopy system, and a lighting system configured to illuminate a biological material. The method includes processing the image object, using the AJ/ML system to determine a presence of a retrievable target cell. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target cell. The method includes confirming the robotic pipettor's location at the target physical orientation, using AI/ML system.

Abstract: A method for automated, artificial-intelligence-based egg identification using optical coherence tomography (OCT) includes positioning an OCT imaging system head in proximity to a biological sample containing an oocyte, wherein the OCT is operatively coupled to an artificial intelligence/machine learning system (AI/ML system) and an imaging system, wherein the imaging system includes a camera system, and a lighting system. The method includes creating at least one three-dimensional image of the oocyte using the OCT, AI/ML system, and imaging system. The method includes using the AI/ML system to analyze the three-dimensional image, wherein an analysis includes detection of a polar body's presence or absence based at least in part on planar views of the oocyte.

Abstract: A method for automated ICSI includes receiving at least one droplet containing an egg in a dish placed on a stage. The method includes using an artificial intelligence/machine learning system (AI/ML system) and an imaging system to detect a zona pellucida. The imaging system includes a microscopy system, a camera system, and a lighting system. The method includes holding the egg using a robotic microtool and lowering a robotic pipettor into the droplet. The method includes using the AI/ML system and imaging system to determine an area at which to hold the egg and positioning the robotic microtool to that area. The method includes using the AI/ML system and imaging system to instruct the robotic microtool to apply negative pressure to hold the egg to the robotic pipettor. The method includes using the AI/ML system and imaging system to determine a target location where zona ablation should be performed.

Abstract: A method of artificial-intelligence-based robotic vitrification includes placing at least one oocyte in a buffer or CPA solution. The method includes processing an image object produced by an imaging system, using an AI/ML system, to determine a location of a retrievable target oocyte. The method includes positioning a robotic pipettor at a target physical orientation relative to the retrievable target oocyte. The method includes confirming the robotic pipettor's location at the target physical orientation, using the AI/ML system. The method includes instructing the robotic pipettor to initiate contact with the retrievable target oocyte and apply negative pressure to secure the oocyte to the robotic pipettor. The method includes using a robotic microtool holder to lower a vitrification assembly or cryo-device of any other make into a buffer or CPA solution dish to a position in proximity to the target physical orientation.

Abstract: A method for automated, artificial-intelligence-based IVF microtool control includes receiving a command at a controller associated with an artificial intelligence/machine learning system (AI/ML system) and an imaging system used at least in part to operate robotic components of in vitro fertilization (IVF) module. The method includes retrieving at least one IVF microtool assembly (MA) from a tool inventory location using a first robotic mechanism of the robotics system, based at least in part on the command. The method includes retrieving at least one IVF receptacle using a second robotic mechanism of the robotics system based at least in part on the command. The method includes placing the IVF receptacle on a stage within the IVF module based on the command. The method includes positioning the at least one MA in proximity to the IVF receptacle, using the first robotic mechanism, based at least in part on the command.

Abstract: A method of artificial-intelligence-based robotic pipetting for spermatozoa preparation includes positioning a vessel containing a semen sample on a staging mechanism, wherein the staging mechanism includes at least one motor to position the vessel at a specified orientation, using an artificial intelligence/machine learning (AI/ML system) system. The method includes confirming a position of the vessel, using the AI/ML system. The method includes directing a robotic pipettor to place a pipette tip within the semen sample at a position specified by the AI/ML system. The method includes aspirating and mixing the semen sample using the robotic pipettor. The method includes receiving an image object from an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes determining a semen liquefaction state of the semen sample based at least in part on the image object, using the AI/ML system.

Abstract: A method of artificial-intelligence-based robotic pipetting for spermatozoa preparation includes positioning a vessel containing a semen sample within a staging mechanism. The method includes using a robotic pipettor to make at least two droplets within a dish on the staging mechanism. The method includes using the robotic pipettor to connect the at least two droplets with a medium channel according to a programmable design commanded by an artificial intelligence/machine learning system (AI/ML system). The method includes using the robotic pipettor to deposit a quantity of sperm from the semen sample into one of the droplets. The method includes using the AI/ML system to optically scan the quantity of sperm to produce first and second image objects using an imaging system that includes a microscopy system, a camera system, and a lighting system. Creation of the image objects is separated by a specified time duration.

Abstract: A method for automated, artificial-intelligence-based denudation includes scanning, robotically, a dish containing a cumulus-oocyte-complex (COC) using an imaging system and an artificial intelligence/machine learning system (AI/ML system) to create an image object. The imaging system includes a microscopy system, a camera system, and a lighting system. The dish includes an enzyme to remove cumulus cells from an oocyte among the COC. The method includes identifying the COC within the image object, and a location, based on comparing the image object to a threshold using the AI/ML system. The threshold is an optical pattern with a probability of corresponding to a COC mass. The method includes instructing a robotic pipettor to collect the COC at the location. The method includes iteratively collecting the COC within the robotic pipettor and expelling the COC to return it to the dish until the AI/ML system confirms sufficient removal of cumulus and corona cells.

Abstract: The invention relates to a method that allows a set of embryos to be ranked on the basis of ploidy potential and/or pregnancy generation potential, to aid the process of selecting embryos for transfer in an in-vitro fertilisation procedure. The method measures properties or characteristics of the entire blastocyst; extracts characteristics by identifying different cell types, mainly blastocyst structures and patterns, without extracting characteristics of the first cell divisions and the behaviour thereof over time; and predicts the prognosis of pregnancy and/or ploidy (result of genetic study and successful implantation), using micrographs standardised for the management thereof and by means of sequential preprocessing and machine learning algorithms implemented in a computer in order to rank the potential of a set of embryos, to obtain a successful, live, full-term pregnancy.

Abstract: The invention relates to a method that allows a set of embryos to be ranked on the basis of ploidy potential and/or pregnancy generation potential, to aid the process of selecting embryos for transfer in an in-vitro fertilisation procedure. The method measures properties or characteristics of the entire blastocyst; extracts characteristics by identifying different cell types, mainly blastocyst structures and patterns, without extracting characteristics of the first cell divisions and the behaviour thereof over time; and predicts the prognosis of pregnancy and/or ploidy (result of genetic study and successful implantation), using micrographs standardised for the management thereof and by means of sequential preprocessing and machine learning algorithms implemented in a computer in order to rank the potential of a set of embryos, to obtain a successful, live, full-term pregnancy.

Abstract: The present invention is providing a system based on artificial vision, and artificial intelligence that is capable of assisting the embryologist to select the best spermatozoa to be injected during an ICSI procedure, requiring the selection of a single sperm. It can identify the best spermatozoa from a sample, in real-time, based on their morphological and motility characteristics which are observed under the microscope at magnifications equal to or above 20×. Is an automatic analysis of sequences of images produced by a digital camera attached to a microscope in real-time. It uses computer vision algorithms to automatically detect and track each of the sperms present on every image of a video and compute a number of features related to the morphological characteristics and motility parameters. The features of each sperm are processed and then evaluated using a mathematical model which determines the quality of each sperm and ranks them accordingly.