Abstract: Systems and methods for cortical mapping to optimize placement of a neural interface in a brain of a patient are provided. A method for cortical mapping of a brain of a patient includes implanting an electrode array proximate to the brain of the patient at a position. The electrode array may include a flexible substrate and a plurality of electrodes arranged on the flexible substrate. The method may further include monitoring and electrophysiological mapping of the brain by causing the patient to perform, attempt to perform, or imagine performing an action over a period of time, recording, via the electrode array, neural activity exhibited by the patient in response to the action or imagined action performed by the patient, decoding the neural activity to determine a correspondence between the neural activity and the action, and determining a level of confidence, wherein the level of confidence is based on the correspondence.

Abstract: A system for performing spinal cord stimulation on a subject is disclosed. The system comprises one or more recording arrays, at least one stimulation array, and a processor. The recording arrays, which may be minimally invasively inserted to a target recording site of the brain, include recording electrodes having a diameter of less than about 1 mm and having a spacing of less than about 1 mm therebetween. The stimulation array, which may be minimally invasively inserted to a target stimulation site within the nervous tissue, includes stimulation electrodes. The processor is configured to receive more recorded signals from the recording arrays at the target recording site, determine an electrophysiological state of the brain based on the recorded signals, and deliver electrical stimulation to the target stimulation site through the stimulation array based on the determined electrophysiological state in order to affect the electrophysiological state.

Abstract: A system for compressing neural signal data using spatiotemporal data compression techniques. The system includes an implantable neural that receives electrophysiologic signal data from the electrode array, maps the electrophysiologic signal data to locations of each of the plurality of the electrodes, and compresses the mapped electrophysiologic signal data based on the locations of the plurality of electrodes utilizing a spatiotemporal data compression algorithm to define compressed electrophysiologic signal data. The implantable neural device can be connectable to a computer system that decompressed the electrophysiologic data for various downstream applications.

Abstract: The present disclosure relates to methods and systems of determining swimming metrics of a user during a swimming session. The method can include receiving, by a processor circuit of a user device, motion information from one or more motion sensors of the user device; determining, by the processor circuit using the motion information, a first set of rotational data of the user device, wherein the first set of rotational data is expressed in a first frame of reference; converting, by the processor circuit, the first set of rotational data into a second set of rotational data, wherein the second set of rotational data is expressed in a second frame of reference; determining, by the processor circuit, one or more swimming metrics of the user; and outputting the one or more swimming metrics.

Abstract: One example method for biomarker detection in digitized pathology samples includes receiving a plurality of image patches corresponding to an image of a pathology slide having a hematoxylin and eosin-stained (“H&E”) stained sample of tissue, each image patch representing a different portion of the image; for each image patch, determining, using a first trained machine learning (“ML”) model, a patch biomarker status; and determining, using a second trained ML model, a tissue sample biomarker status for the sample of tissue based on the patch biomarker statuses of the image patches.

Abstract: One example method includes obtaining one or more histopathology images of a sample from a cancer patient; selecting a plurality of tissue image patches from the one or more histopathology images; determining, by a deep learning system comprising a plurality of trained machine learning (ML) models, a plurality of image features for the plurality of tissue image patch, wherein each tissue image patch is analyzed by one of the trained ML models; determining, by the deep learning system, probabilities of patient survival based on the determined plurality of image features; and generating, by the deep learning system, a prediction of patient survival based on the determined probabilities.

Abstract: One example method includes receiving a digital image of a needle core prostate biopsy, displaying, using a display device, a magnified portion of the digital image, obtaining, from a deep learning model, Gleason scores corresponding to patches of the magnified portion of the digital image, and displaying, using the display device, a superimposed overlay on the magnified portion of the digital image based on the Gleason scores and corresponding confidence values of the Gleason scores, the superimposed overlay comprising one or more outlines corresponding one or more Gleason scores associated with the magnified portion of the digital image and comprising image patches having colors based on a Gleason score of the Gleason scores corresponding to an underlying portion of the magnified portion of the digital image and a confidence value of the corresponding Gleason score.

Abstract: Systems and methods of analyzing a user's motion during a swimming session are described. One or more motions sensors can collect motion data of the user. A processor circuit can make motion analysis based on the motion data. The processor circuit can determine if the user's arm swing is a genuine swim stroke. The processor circuit can also determine whether the user is swimming or turning. The processor circuit can also classify the user's swim stroke style. The processor circuit can also determine the user's swim stroke phase. The processor circuit can also determine the user's stroke orbit consistency.

Abstract: One example method for AI prediction of prostate cancer outcomes involves receiving an image of prostate tissue; assigning Gleason pattern values to one or more regions within the image using an artificial intelligence Gleason grading model, the model trained to identify Gleason patterns on a patch-by-patch basis in a prostate tissue image; determining relative areal proportions of the Gleason patterns within the image; assigning at least one of a risk score or risk group value to the image based on the determined relative areal proportions; and outputting at least one of the risk score or the risk group value.

Abstract: Systems and methods are disclosed for tracking physiological states and parameters for calorie estimation. A start of an exercise session associated with a user of a wearable computing device is determined. Heart rate data is measured for a first period of time. An onset heart rate value of the user is determined based on the measured heart rate data, the onset heart rate value associated with a lowest valid heart rate measured during the first period of time. A resting heart rate parameter (RHR) of a calorimetry model is associated with at least one of the onset heart rate value, a preset RHR, and an RHR based on user biometric data. Energy expenditure of the user during a second period of time is estimated based on the calorimetry model and a plurality of heart rate measurements obtained by the wearable computing device during the second period of time.

Abstract: Systems and methods are disclosed for tracking physiological states and parameters for calorie estimation. A start of an exercise session associated with a user of a wearable computing device is determined. Heart rate data is measured for a first period of time. An onset heart rate value of the user is determined based on the measured heart rate data, the onset heart rate value associated with a lowest valid heart rate measured during the first period of time. A resting heart rate parameter (RHR) of a calorimetry model is associated with at least one of the onset heart rate value, a preset RHR, and an RHR based on user biometric data. Energy expenditure of the user during a second period of time is estimated based on the calorimetry model and a plurality of heart rate measurements obtained by the wearable computing device during the second period of time.

Abstract: A set of methods is described for identifying optimal repetition rates for certain repetitive processes, and identifying musical selections with tempi matched to those optimal rates, so as to use synchrony with musical rhythms as a guide to optimizing performance in repetitive physical, biomechanical, and physiologic processes.

Abstract: A method of estimating the maximal oxygen uptake of an individual on the basis of heart rate data, biometric data, biomechanical data, and geophysical data is described. These data can be collected as the individual engages in activities requiring various levels of exertion, without modifying those activities from the ordinary manner in which they are performed. In particular, in some embodiments the method described here obviates the conventional need for a laboratory setting when estimating maximal oxygen uptake and the method can be applied to estimate maximal oxygen uptake under more natural conditions than conventional testing protocols requiring treadmills or stationary ergometers typically permit. Furthermore, it is described how such estimates of maximal oxygen uptake can be used to estimate other quantities of interest, including fat and carbohydrate metabolism, lactate production, and water and electrolyte loss during exercise.