Abstract: Systems and methods for implementing machine-learning models for ovarian stimulation is described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation process may include receiving patient-specific data associated with a patient, and predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data, where the at least one predictive model is trained using prior patient-specific data associated with a plurality of prior patients.

Abstract: Systems and methods for predicting viability of one or more embryos is described herein. In some variations, a method may include receiving a single image of the embryo via a real-time communication link with an image capturing device and generating a viability score for the embryo by classifying the single image via at least one convolutional neural network. In some variations, a method may include receiving a plurality of single images, where each single image depicts a different respective embryo of a plurality of embryos, generating a viability score for each embryo by classifying each single image via at least one convolutional neural network, and ranking the plurality of embryos based on the viability scores for the plurality of embryos.

Abstract: Systems and methods for implementing machine-learning models for ovarian stimulation is described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation process may include receiving patient-specific data associated with a patient, and predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data, where the at least one predictive model is trained using prior patient-specific data associated with a plurality of prior patients.

Abstract: A robotic system includes control circuitry configured to cause actuation of one or more actuators of each of a first robotic arm and a second robotic arm. The control circuitry is configured to determine a position of a first end effector of the first robotic arm and a position of a second end effector of the second robotic arm, the positions of the first end effector and the second end effector forming a virtual rail, receive manual positioning input for the first robotic arm based at least in part on sensor signals from one or more sensors of the first robotic arm, and in response to the manual positioning input, generate a first movement command to move the first robotic arm in accordance with the manual positioning input and generate a second movement command to move the second robotic arm in a manner as to maintain at least one of a position or orientation of the second end effector relative to a point on the virtual rail.

Abstract: Systems and methods for moving or manipulating robotic arms are provided. A group of robotic arms are configured to form a virtual rail or line between the end effectors of the robotic arms. The robotic arms are responsive to outside force such as from a user. When a user moves a single one of the robotic arms, the other robotic arms will automatically move to maintain the virtual rail alignments. The virtual rail of the robotic arm end effectors may be translated in one or more of three dimensions. The virtual rail may be rotated about a point on the virtual rail line. The robotic arms can detect the nature of the contact from the user and move accordingly. Holding, shaking, tapping, pushing, pulling, and rotating different parts of the robotic arm elicits different movement responses from different parts of the robotic arm.

Abstract: Systems and methods for implementing machine-learning models for ovarian stimulation is described herein. In some variations, a computer-implemented method may include optimizing an ovarian stimulation process may include receiving patient-specific data associated with a patient, and predicting an egg outcome for the patient for each of a plurality of treatment options for an ovarian stimulation process based on at least one predictive model and the patient-specific data, where the at least one predictive model is trained using prior patient-specific data associated with a plurality of prior patients.

Abstract: Systems and methods for moving or manipulating robotic arms are provided. A group of robotic arms are configured to form a virtual rail or line between the end effectors of the robotic arms. The robotic arms are responsive to outside force such as from a user. When a user moves a single one of the robotic arms, the other robotic arms will automatically move to maintain the virtual rail alignments. The virtual rail of the robotic arm end effectors may be translated in one or more of three dimensions. The virtual rail may be rotated about a point on the virtual rail line. The robotic arms can detect the nature of the contact from the user and move accordingly. Holding, shaking, tapping, pushing, pulling, and rotating different parts of the robotic arm elicits different movement responses from different parts of the robotic arm.

Abstract: Systems and methods for moving or manipulating robotic arms are provided. A group of robotic arms are configured to form a virtual rail or line between the end effectors of the robotic arms. The robotic arms are responsive to outside force such as from a user. When a user moves a single one of the robotic arms, the other robotic arms will automatically move to maintain the virtual rail alignments. The virtual rail of the robotic arm end effectors may be translated in one or more of three dimensions. The virtual rail may be rotated about a point on the virtual rail line. The robotic arms can detect the nature of the contact from the user and move accordingly. Holding, shaking, tapping, pushing, pulling, and rotating different parts of the robotic arm elicits different movement responses from different parts of the robotic arm.

Abstract: Systems and methods for moving or manipulating robotic arms are provided. A group of robotic arms are configured to form a virtual rail or line between the end effectors of the robotic arms. The robotic arms are responsive to outside force such as from a user. When a user moves a single one of the robotic arms, the other robotic arms will automatically move to maintain the virtual rail alignments. The virtual rail of the robotic arm end effectors may be translated in one or more of three dimensions. The virtual rail may be rotated about a point on the virtual rail line. The robotic arms can detect the nature of the contact from the user and move accordingly. Holding, shaking, tapping, pushing, pulling, and rotating different parts of the robotic arm elicits different movement responses from different parts of the robotic arm.

Abstract: Systems and methods for moving or manipulating robotic arms are provided. A group of robotic arms are configured to form a virtual rail or line between the end effectors of the robotic arms. The robotic arms are responsive to outside force such as from a user. When a user moves a single one of the robotic arms, the other robotic arms will automatically move to maintain the virtual rail alignments. The virtual rail of the robotic arm end effectors may be translated in one or more of three dimensions. The virtual rail may be rotated about a point on the virtual rail line. The robotic arms can detect the nature of the contact from the user and move accordingly. Holding, shaking, tapping, pushing, pulling, and rotating different parts of the robotic arm elicits different movement responses from different parts of the robotic arm.

Abstract: Systems and methods for moving or manipulating robotic arms are provided. A group of robotic arms are configured to form a virtual rail or line between the end effectors of the robotic arms. The robotic arms are responsive to outside force such as from a user. When a user moves a single one of the robotic arms, the other robotic arms will automatically move to maintain the virtual rail alignments. The virtual rail of the robotic arm end effectors may be translated in one or more of three dimensions. The virtual rail may be rotated about a point on the virtual rail line. The robotic arms can detect the nature of the contact from the user and move accordingly. Holding, shaking, tapping, pushing, pulling, and rotating different parts of the robotic arm elicits different movement responses from different parts of the robotic arm.