Abstract: A method is provided for deep learning for modeling disease progression. The method may include generating, by a machine learning model, a first feature representation based on clinical data associated with a baseline cognitive state of a patient. The method may also include generating, by the machine learning model, a second feature representation based on an image of a brain of the patient. The method may also include generating, by the machine learning model, a set representation by at least fusing the first feature representation and the second feature representation. The method may also include predicting, by the machine learning model, a change in the baseline cognitive state over a time period based at least on the set representation. Related systems and articles of manufacture are also disclosed.

Abstract: One aspect of the invention provides a fetal pulse oximeter including: a shape-memory member adapted and configured to expand outward and define a loop when advanced out of a cannula; one or more light sources mounted on the shape memory member and facing toward a center of the loop, the one or more light sources adapted and configured to generate red and infrared light; and a photodiode mounted on the shape memory member and facing toward a center of the loop. Another aspect of the invention provides a method for measuring recording pulse and blood oxygen saturation. The method includes: advancing the fetal pulse oximeter as described herein out of a cannula within a placenta; allowing the shape-memory member to expand outward; and placing the loop over a limb.