Abstract: Certain aspects relate to systems and techniques for medical instrument navigation and targeting. In one aspect, a system includes a medical instrument having an elongate body and at least one sensor, a display, a processor, and a memory storing a model of a mapped portion of a luminal network and a position of a target with respect to the model. The processor may be configured to: determine, based on data from the at least one sensor, a position and orientation of a distal end of the medical instrument with respect to the model, and cause, on at least a portion of the display, a rendering of the model, the position of the target, and the position and orientation of the distal end of the medical instrument. The rendering may be based on a viewpoint directed at the target and different from a viewpoint of the medical instrument.

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: 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: Certain aspects relate to systems and techniques for medical instrument navigation and targeting. In one aspect, a system includes a medical instrument having an elongate body and at least one sensor, a display, a processor, and a memory storing a model of a mapped portion of a luminal network and a position of a target with respect to the model. The processor may be configured to: determine, based on data from the at least one sensor, a position and orientation of a distal end of the medical instrument with respect to the model, and cause, on at least a portion of the display, a rendering of the model, the position of the target, and the position and orientation of the distal end of the medical instrument.

Abstract: Certain aspects relate to systems and techniques for medical instrument navigation and targeting. In one aspect, a system includes a medical instrument having an elongate body and at least one sensor, a display, a processor, and a memory storing a model of a mapped portion of a luminal network and a position of a target with respect to the model. The processor may be configured to: determine, based on data from the at least one sensor, a position and orientation of a distal end of the medical instrument with respect to the model, and cause, on at least a portion of the display, a rendering of the model, the position of the target, and the position and orientation of the distal end of the medical instrument. The rendering may be based on a viewpoint directed at the target and different from a viewpoint of the medical instrument.

Abstract: Certain aspects relate to systems and techniques for medical instrument navigation and targeting. In one aspect, a system includes a medical instrument having an elongate body and at least one sensor, a display, a processor, and a memory storing a model of a mapped portion of a luminal network and a position of a target with respect to the model. The processor may be configured to: determine, based on data from the at least one sensor, a position and orientation of a distal end of the medical instrument with respect to the model, and cause, on at least a portion of the display, a rendering of the model, the position of the target, and the position and orientation of the distal end of the medical instrument.