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.

This disclosure provides methods for diagnosing a cognitive disorder including performing a tactile perception test on a subject, measuring the subject's tactile perception test performance with a microelectromechanical sensor, and performing a cognitive disorder diagnosis on the subject. The tactile perception test may include distinguishing between various shaped and sized objects and selecting a predetermined one. The tactile perception test may include a two-point discrimination test. The tactile perception test may include testing lower extremity coordination. The tactile perception test may include testing upper extremity coordination. The cognitive disorder may include dementia, Alzheimer's, brain trauma, or concussion. The microelectromechanical sensor may include an accelerometer or gyroscope.

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 system and method for quantifying Alzheimer's disease (AD) risk using one or more interferometric micro-Doppler radars (IMDRs) and deep learning artificial intelligence to distinguish between cognitively unimpaired individuals and persons with AD based on gait analysis. The system utilizes IMDR to capture signals from both radial and transversal movement in three-dimensional space to further increase the accuracy for human gait estimation. New deep learning technologies are designed to complement traditional machine learning involving separate feature extraction followed-up with classification to process radar signature from different views including side, front, depth, limbs, and whole body where some motion patterns are not easily describable. The disclosed cross-talk deep model is the first to apply deep learning to learn IMDR signatures from two perpendicular directions jointly from both healthy and unhealthy individuals. Decision fusion is used to integrate classification results from feature-based classifier and deep learning AI to reach optimal decision.

In an approach to monitoring and querying autobiographical events, one or more computer processors receive data from one or more sensors associated with a user. Based on the received data from the one or more sensors, one or more computer processors detect an autobiographical event associated with the user. One or more computer processors convert the received data from the one or more sensors into one or more observations associated with the autobiographical event. One or more computer processors convert the one or more observations into one or more questions associated with the one or more observations. One or more computer processors query the user with the one or more questions. One or more computer processors receive at least one response to the one or more questions from the user.

The present invention relates to a method of providing diagnostic information for Alzheimer's disease using a brain network.

A system and method for patient movement detection and fall monitoring to address the need to proactively monitor patients to detect abnormal movements, in-room activity, and other movements associated with providing in-room care. The system comprises an environmental model which can be used to track the position and movement of a patient and a classifier network configured to receive movement data and classify a patient's movement as normal or abnormal movement. In addition to monitoring in-room activity, the system and method create safe zones within the room to ensure patients are proactively monitor in the event of a seizure, fall, or other unintended activity. The system will record and store in-room video in a secure environment. Videos and notifications are automatically sent to designated staff as events occur.

A detection device detecting changes in positive and negative feelings related to mild cognitive impairment is provided. This detection device includes: a detector detecting heartbeat information of a subject; a calculating unit calculating a maximum Lyapunov exponent from the heartbeat information, the maximum Lyapunov exponent indicating to what extent heartbeat intervals vary; a feeling determination unit determining whether the subject has a negative feeling with brain fatigue, anxiety or depression, or a positive feeling without brain fatigue, anxiety and depression, based on the maximum Lyapunov exponent; a change determination unit determining whether the positive or negative feeling of the subject has changed, based on frequency of occurrence of the negative or positive feeling in a predetermined period; and an output unit outputting a result of determination by the change determination unit.

Methods of assessing or obtaining an indication of the presence of a cognitive disorder by analysing microstructural changes in regions of the brain are provided. The invention particularly relates to methods of assessing or obtaining an indication of the presence of types of dementia, for example Alzheimer's disease, by analysing changes in minicolumns in regions or layers of the cortex of the brain or of the whole brain.

Body fluid management systems and associated methods include in-line measurement and/or detection of analytes, contaminants, and/or physical characteristics for the diagnosis or detection of disease, infection, or toxicity.