The present disclosure provides methods for diagnosing and determining the disease progression of neurodegenerative disorders in patients using neurophysiological biomarkers.

A dementia determination system includes: an obtainer that obtains a consecutive press ratio which is a ratio of a total number of consecutive presses of a button of an operating device to a total number of presses of the button of the operating device, the consecutive presses being presses of the button made at time intervals each shorter than or equal to a reference time interval, the operating device being used by a user to operate an electric device; a determiner that determines a dementia level of the user based on the consecutive press ratio obtained by the obtainer; and an outputter that outputs dementia information indicating the dementia level determined by the determiner, wherein the determiner determines that the dementia level is higher as the consecutive press ratio is higher.

Medical device systems and methods are disclosed herein for assessing motor symptoms in Parkinson's disease patients through passively and/or actively acquired sensor data of motor behaviors and generating motor system assessments. Passive movement data and/or active motor performance data collected from Parkinson's disease patients is representative of EQ-5D-5L, PDQ-39, and/or MDS-UPDRS health status measurement data to more accurately predict quality of life, health status, and disease severity of Parkinson's disease patients. The generated motor system assessment may be used to provide neurologists and other health professionals with more accurate treatment recommendations and disease management protocols rather than using patient surveys alone.

A method of determining a condition of a user, the method may include: monitoring one or more activities performed by a user during a predetermined monitoring time period; determining one or more activity complexity datasets each for one of the one or more monitored activities; determining a user complexity model based on at least one of the one or more activity complexity datasets; determining a distance vector between the user complexity model and a reference user complexity model; and determining a condition of the user based on the distance vector and a reference distance-condition model.

A method of quantitatively evaluating a cognitive status and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive status and predict its future change based on the at least one feature obtained.

A tool for test-based updating of actuarial tables includes an analyzer, a statistical transformer and an actuarial analyzer. The analyzer analyzes the results of cognitive and physical tests performed on a candidate of a known age and generates cognitive and physical scores for the results. The statistical transformer utilizes the scores and the known age to generate a statistical shift and generates adjusting factors F.sub.i for the actuarial tables from the statistical shift. The actuarial analyzer raises the mortality probabilities of the actuarial tables by a power of 1/F.sub.i per table, thereby to produce personalized actuarial tables for the candidate.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to 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.

A method and apparatus for tracking disease progression as revealed by multiple lesions perform a global optimization to identify corresponding lesions by overlap, for example, after outlines of the lesions have been morphologically dilated. A clustering algorithm addresses the problem of lesions separating into parts or joining together to provide a clear picture of disease progression.

Embodiments relate to an audio system for various audio applications. The audio system registers the locations of one or more sound sources and selects the target sound source based on a hidden Markov model. A health monitoring system that integrates an audio system may use information collected by sensors to monitor an amount of social interaction of a user and predict a risk of dementia and/or hearing loss based on a model. The audio system uses a current/voltage sensor to detect electrical drive signals for determining a level of audio leakage of the audio system. Additionally, the audio system may update a video stream with an audio background based on an artificial visual background in the video stream so that the updated video stream sounds as if it originated from the user being located in a physical representation related to the background.

Examples herein include systems for detecting cheating and changes in mental performance in card games, including bridge. The system can acquire board data for multiple events, the events including bridge games. For each event, determining performance values for a player based on the board and play data, wherein the performance values may additionally be based in part on timing data. The system can detect a deviation of by comparing the performance values against a threshold, wherein the threshold is based on past performance of known cheating players or the individual player. Then the system can alert a user when the likelihood of cheating or other changes exceeds a threshold.