Abstract: In accordance with some embodiments, systems, methods, and media for intrapartum prediction of unfavorable labor outcomes are provided. In some embodiments, a system comprises a processor programmed to: generate a feature vector including static variables knowable when the patient goes into labor, and dynamic variables including a recent cervical dilation; provide the feature vector to a machine learning model trained using labeled feature vectors associated with patients with known labor outcomes, each labeled vector including static and dynamic variables including cervical dilation in the same range as the patients, and each labeled feature vector indicating whether one or more unfavorable outcomes was experienced; receive, from the model, a risk the patient will experience an unfavorable outcome; and cause information indicative of that risk to be presented to aid the user in determining whether to recommend intrapartum Cesarean delivery for the patient.

Abstract: The present disclosure describes systems and methods for mapping a planar surface to a curved surface. One such method comprises providing a planar lattice pattern having identical regular polygon shapes which can be truss support modules for supporting a curved surface structure. Method further include determining respective positions of the nodal points of the identical polygon shapes by placing a starting nodal point on an axis of symmetry of the planar lattice pattern and iteratively positioning each nodal point in relation to already placed neighboring nodes to make a distance between a currently positioned nodal point optimally close with a set value of the planar lattice pattern. Method still further comprises forming a curved lattice pattern of the truss support module based on the positions of the nodal points of the identical polygon shapes.