In a method for monitoring absorption of a transmitter output irradiated into a patient by a transmitter unit of a magnetic resonance device, absorption data is provided, which describes a patient-nonspecific, location-dependent absorption sensitivity of the transmitter output to be irradiated. The patient is positioned in an irradiation region of the magnetic resonance device, in which the irradiation of the transmitter output into the patient is to take place. An anatomy of the patient is detected in the irradiation region, and the absorption data is assigned to the anatomy of the patient. A magnetic resonance scan of the patient is then performed, wherein the transmitter output absorbed by the patient is monitored during the magnetic resonance scan based on the absorption data assigned to the anatomy of the patient.

Disclosed herein is a hair health assessment, treatment, and styling diagnosis and recommendation method and system. The techniques use science and technology in order to aid users in achieving optimal health and beauty of their hair. The present disclosure allows a user to provide hair samples, such as digital images of hair, that can be analyzed using Machine Learning (ML) and Artificial Intelligence (AI) technology to provide recommendations including hair health assessments, hair care regimes, hair care products, and hair care services in an accurate, efficient, and automated manner. The present disclosure may employ a digital image capture device, either stand alone device or an embedded camera of a mobile device or a microscopic attachment, in order to capture digital images of their hair at a smartphone camera magnification level from a selfie image or microscopic-level of magnification, as a hair sample to be analyzed by the system.

A system monitors an individual for conditions indicating a possibility of occurrence of irregular heart events. A database includes a plurality of combinations of at least a first signature and a second signature. A first portion of the plurality of combinations is associated with a normal heartbeat and a second portion of the plurality of combinations is associated with an irregular heart event. A wearable heart monitor that is worn on a body of the patient includes a heart sensor for generating a heart signal responsive to monitoring a beating of a heart of the individual. The monitor further includes a processor for receiving the heart signal from the heart sensor. The processor is configured to analyze the heart signal using a plurality of different processes. Each of the plurality of different processes generates at least one of the first signature and the second signature. The plurality of different processes provide a unique combination including at least the first signature and the second signature for the generated heart signal. The processor compares the unique combination with the plurality of combinations in the database, locates a combination of the plurality of combinations that substantially matches the unique combination and generates a first indication if the unique combination substantially matches one of the first portion of the plurality of combinations and a second indication if the unique combination substantially matches one of the second portion of the plurality of combinations.

The present disclosure provides systems and methods that include or otherwise leverage a machine-learned brain injury location model to predict locations of brain injury in a patient based on test data associated with the patient, such as, for example, behavioral test data. For example, the machine-learned brain injury location model can be trained on training data associated with a corpus of patients, where the training data includes sets of example test data (e.g., behavioral test data) respectively labeled with ground truth brain injury locations.

A method for monitoring a patient in a bed using a camera. The method includes identifying a boundary of the bed using data from the camera, identifying parts of the patient using data from the camera, and determining an orientation of the patient using the parts identified for the patient. The method further includes monitoring movement of the patient using the parts identified for the patient and computing a departure score indicating the likelihood of the patient departing the bed based on the orientation of the patient and the movement of the patient. The method further includes comparing the departure score to a predetermined threshold and generating a notification when the departure score exceeds the predetermined threshold.

Systems and methods for enhancing infection detection and monitoring through decomposed physiological data are disclosed. An example method includes receiving, from a wearable device of a user, physiological data of the user and decomposing the physiological data, by applying a heart rate algorithm, to generate one or more physiological parameters. The example method further includes analyzing, by applying the heart rate algorithm, the one or more physiological parameters to output a period classification, and determining whether or not the period classification is indicative of an infection. The example method further includes, responsive to determining that the period classification is indicative of the infection, displaying, in a user interface, a warning to the user that indicates the infection, and receiving, from the wearable device of the user, additional physiological data of the user to monitor the infection.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing hybrid machine learning. More specifically, the system and method produce predictions of MCI conversions to dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is trained using transfer learning. A platform may then receive a request from a clinician for a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

Systems and methods for detecting data acquisition conditions using color-based penalties can include a computing device obtaining a sequence of images acquired by a photodetector. The computing device can determine, for each pixel position of a plurality of pixel positions associated with the sequence of images, a respective penalty score indicative of a similarity between a color value of a pixel of the pixel position and a desired color value. The desired color value can represent a color property of light emitted from body parts of users when placed opposite to the photodetector. The computing device can determine, using penalty scores of the plurality of pixel positions, a relative position of a body part of a user with respect to a desired position.

Systems and methods for blood pressure estimation using smart offset calibration can include a computing device associating a calibration photoplethysmographic (PPG) signal generated from a first sequence of image frames obtained from a photodetector of the computing device with one or more measurement values generated by a blood pressure measurement device different from the computing device. The computing device can obtain a recording PPG signal generated from a second sequence of image frames obtained from the photodetector, and identify a calibration model from a plurality of blood pressure calibration models based on the calibration PPG signal and the recording PPG signal. The computing device can generate a calibrated blood pressure value using the recording PPG signal, features associated with the calibration PPG signal and the identified calibration model.

Disclosed are devices and methods for estimating blood pressure, which implement a pulse-transit-time-based blood pressure model that can be calibrated. Some implementations provide reliable and user friendly means for calibrating the blood pressure model using blood pressure perturbation methods and multiple sensors.