An image processing method or the like suitable for analyzing magnetic resonance image data using simple calculation is presented. Image processing apparatus processes magnetic resonance image data acquired by scanning multiple regions of a living body. MRI imaging apparatus scanning multiple regions of the living body to acquire magnetic resonance image data. Image processing unit generates phase difference image data from the magnetic resonance image data. Signal acquisition unit acquires a phase difference image signal from the phase difference image data. Statistic calculation unit performs statistical processing of the distribution of the phase difference image signal with respect to the phase difference for each region to calculate a statistic, for example. Examples of the statistical processing includes calculation of an average, a standard deviation, kurtosis, skewness, etc. Target material evaluation unit evaluates the amount of the target material included in multiple regions using the statistic for each region.

Methods of categorisation of schizophrenia sufferers into subtypes based on changes in brain morphology, together with associated blood biomarkers are provided. The methods allow for more accurate treatment and diagnosis of schizophrenia.

A viewing navigation grid and imaging display especially useful for viewing recorded skin images.

Systems and methods for sleep apnea and nocturnal hypoxia detection are described. In one non-limiting example, a pulse oximetry device can include a sensor and a computing device. The computing device can be attached to a patient for a sleep apnea diagnosis. The computing device can be configured to generate pulse oximetry data by measuring pulse oximetry of a patient with the sensor. Multiple apnea indicators can be identified from the pulse oximetry data and from information associated with the patient. The multiple sleep apnea indicators can be provided to a machine learning model trained for sleep apnea prediction. A sleep apnea classification can be determined from the machine learning model.

An ear-wearable electronic device comprises a motion sensor configured to generate motion information and a first respiration rate estimated using the motion information. A

PPG sensor is configured to generate PPG data and a second respiration rate estimate using the PPG data. A processor is configured to produce a respiration rate estimate using the first and second respiration rate estimates. A communication device is configured to communicate the respiration rate estimate to one or both of an external electronic device and a cloud database.

A method of analyzing an image of a volume of tissue to determine a risk of developing breast cancer using a volume averaged sound speed within the volume. A method of determining a response to a treatment plan by determining a volume and a volume averaged sound speed of a region of interest within a volume of breast tissue and generating a combined metric from the volume and the volume averaged sound speed over the plurality of instances of time. A method of analyzing an image of a volume of tissue of a breast by applying a spatial filter to at least one ultrasound tomography image at the computing system and generating a stiffness map from the at least one ultrasound tomography image.

Methods and systems are provided to determine efficacy of a medical treatment by monitoring biomarkers of a patient undergoing the medical treatment. The monitoring is performed by sensors that are attached to the patient while the patient is performing his/her normal life. For example, the sensors can be worn similar to a watch, a headband, a belt, etc.

There is provided a technique configured to measure blood pressure with high accuracy. A pulse wave is acquired from each of a plurality of regions on a body surface of a subject, at least two regions are selected from among the plurality of regions in accordance with signal quality of the acquired pulse wave of each region, and blood pressure information is calculated with reference to pulse wave propagation information indicating pulse wave propagation between the at least two regions selected.

Systems, methods, and computer-readable media are provided for identification of patients having an elevated near-term risk of chronic kidney disease progression, including quantitatively predicting whether or not an elevated risk of progression of Stage 3 or Stage 4 chronic kidney disease is likely within a time interval of up to 36 months subsequent to computing the prediction. Based on the prediction, appropriate care providers may be notified so that the risk of CKD progression may be mitigated. In some embodiments, serial measurements are obtained of urine osmolality, and a challenge with an AVP V2 antagonist and serum sodium concentration is provided. From a time series based on the serial measurements, estimates of each variable's velocity and/or doubling-time may be determined. These values then may be combined via a multivariable mathematical model for providing a leading indicator of near-term future abnormalities in kidney function.

A computer-implemented method, a computer system and a computer program product manage a medical treatment of a care recipient. The method includes obtaining a treatment record for the care recipient from a server. The method also includes capturing real-time biometric data of the care recipient using a plurality of sensors. The method further includes determining a severity level of the adverse event by comparing the real-time biometric data with the treatment record in response to detecting an adverse event in the real-time biometric data. In addition, the method includes generating a recommendation for an alternative treatment in response to the severity level being above a threshold. Lastly, the method includes transmitting a notification comprising the recommendation to a treatment provider.