Abstract: Methods, systems and non-transient computer-readable media are provided for optimizing sensor wear and/or longevity of a personalized model used for estimating glucose values.

Abstract: Methods, systems and non-transient computer-readable media are provided for updating models used for estimating glucose values. For example, technologies are provided for updating an existing population model for estimating glucose values for a population of users to generate a new updated population model for a subset of users of the population of users. As another example, technologies are provided for updating an existing personalized model for estimating glucose values to generate a new updated personalized model that is personalized for a particular user.

Abstract: Disclosed herein are techniques related to glucose estimation without continuous glucose monitoring. In some embodiments, the techniques may involve receiving input data associated with a user. The input data may comprise discrete blood glucose measurement data associated with the user, activity data associated with the user, contextual data associated with the user, or a combination thereof. The techniques may also involve using an estimation model and the input data associated with the user to generate one or more estimated blood glucose values associated with the user.

Abstract: Disclosed herein are techniques related to event-oriented predictions of glycemic responses. In some embodiments, the techniques may involve accessing a prediction model that correlates a person's glycemic responses to events and the person's physiological parameters during the events. The techniques may also involve obtaining a glucose level measurement of the person during an event. Additionally, the techniques may involve determining, based on the glucose level measurement, a physiological parameter of the person during the event. Furthermore, the techniques may involve predicting the person's glycemic response to the event based on applying the prediction model to the physiological parameter.

Abstract: A system for monitoring a patient includes one or more processors and a sensor device implemented in circuitry. The system is configured to measure, using the sensor device, a blood glucose status of the patient, and determine, using one or more processors, an initial insulin dose for the patient based on the blood glucose status of the patient. The system is further configured to optimize the initial insulin dose for the patient, based at least in part on a correction factor, to create an optimized insulin dose for the patient. The system is configured to facilitate therapy, using the one or more processors, based on the determined optimized insulin dose.

Abstract: Data for a particular user can be received from a number of different input channels over a time period. The received data can include discrete blood glucose measurement data, user activity data and other contextual data for the user. The received data can be processed to generate an input data set, which can then be processed along with information from a population model, via a supervised machine learning model, to learn a transfer function for a personal model for the user that estimates blood glucose values for the user by mapping the received data to a sequence of estimated blood glucose values for the user. Parameters of the supervised machine learning model can be adjusted to generate an optimized personal model of the user that estimates blood glucose values for the user by mapping the received data to the sequence of estimated blood glucose values for the user.

Abstract: An optimized population model that estimates blood glucose values for a population of users is generated by mapping received data for the population of users over a time period to a sequence of estimated blood glucose values for the population of users over the time period. Discrete blood glucose measurement data for each user, user activity data for each user, and other contextual data for each user can be processed via a supervised machine learning model to learn a transfer function for a population model that estimates blood glucose values for the population of users. One or more parameters of the learning model can be adjusted to generate the optimized population model.

Abstract: A system and computer-implemented method is provided for uncovering genetic drivers of disease in neurodegeneration and other central nervous system (CNS) diseases for novel therapeutic target identification. The process can start with a first integrated, quantitative “deep” imaging phenotype, that accurately reflects disease at a given time-point (cross-sectionally) and returns candidate single nucleotide polymorphisms (SNPs) and/or genes. The candidate SNPs/genes are further validated, by using a second machine learning and/or artificial intelligence (AD-based image analysis operation to assess clinical response, and may include gene expression profiling, and target plausibility analysis including pathway mapping. A set of candidate SNPs/genes may be constructed to accurately predict the first imaging phenotype with a deep learning model from said SNPs/genes to serve as an additional validation step.

Abstract: The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from molecular Magnetic Resonance Imaging (MRI) data where two groups differ and measuring MRI signal within determined critical areas in a new subject in order to assign risk or diagnosis.

Abstract: The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from PET data where two groups differ and measuring PET signal within determined critical areas in a new subject in order to assign risk or diagnosis.

Abstract: The present disclosure is directed to iterative regularized reconstruction methods. In certain embodiments, the methods incorporate locally-weighted total variation denoising to suppress artifacts induced by PSF modeling. In certain embodiments, the methods are useful for suppressing ringing artifacts while contrast recovery is maintained. In certain embodiments, the weighting scheme can be extended to noisy measures introducing a noise-independent weighting scheme. The present disclosure is also directed to a method for quantifying radioligand binding in a subject without collecting arterial blood. In certain embodiments, the methods incorporate using imaging data and electronic health records to predict one or more anchors, which are used to generate an aterial input function (AIF) for the radioligand.

Abstract: The present disclosure is directed to iterative regularized reconstruction methods. In certain embodiments, the methods incorporate locally-weighted total variation denoising to suppress artifacts induced by PSF modeling. In certain embodiments, the methods are useful for suppressing ringing artifacts while contrast recovery is maintained. In certain embodiments, the weighting scheme can be extended to noisy measures introducing a noise-independent weighting scheme. The present disclosure is also directed to a method for quantifying radioligand binding in a subject without collecting arterial blood. In certain embodiments, the methods incorporate using imaging data and electronic health records to predict one or more anchors, which are used to generate an aterial input function (AIF) for the radioligand.

Abstract: The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from PET data where two groups differ and measuring PET signal within determined critical areas in a new subject in order to assign risk or diagnosis.

Abstract: The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from molecular Magnetic Resonance Imaging (MRI) data where two groups differ and measuring MRI signal within determined critical areas in a new subject in order to assign risk or diagnosis.

Abstract: The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from PET data where two groups differ and measuring PET signal within determined critical areas in a new subject in order to assign risk or diagnosis.

Abstract: The subject matter disclosed herein relates to methods for diagnosing a neurological disorder in a subject. In certain aspects, the methods described herein involve determining one or more critical areas in the brain from PET data where two groups differ and measuring PET signal within determined critical areas in a new subject in order to assign risk or diagnosis.