System and method for detecting cognitive decline in a subject using a classification system for detecting cognitive decline in the subject based on a speech sample. The classification system is trained using speech data corresponding to audio recordings of speech from normal and cognitive decline patients to generate an ensemble classifier comprising a plurality of component classifiers and an ensemble module. Each of the plurality of component classifiers is a machine-learning classifier configured to generate a component output identifying a sample data as corresponding to a normal patient or a cognitive patient. The machine-learning classifier is generated based on a subset of available features. The ensemble module receives component outputs from all of the component classifiers and generates an ensemble output identifying the sample data as corresponding to a normal or cognitive decline patient based on the component outputs.

This disclosure provides systems and methods for mapping and/or measuring a mechanical property of a medium. The mechanical property can be measured by Brillouin spectroscopy. The systems and methods can include a three-dimensional imaging modality that is co-registered with a Brillouin probe beam of a Brillouin spectrometer. The three-dimensional imaging modality can be optical coherence tomography or Scheimpflug camera imaging.

The present application discloses a vibrational spectroscopy method for diagnosing Alzheimer's disease in a subject comprising the steps of a) determining in a Raman spectrum of a biofluid from said subject or in a sample derived from said biofluid the intensity of a Raman band around 1256 cm.sup.1; and b) comparing the intensity value obtained in step a) with a reference value, wherein an increase in the value of said Raman band intensity with respect to the reference value is indicative that the subject suffers Alzheimer's disease. The application also discloses an apparatus for plasma sample analysis, a computer program, a data carrier and to a therapy suitable for treating Alzheimer's disease.

A system for processing a medical image for personalized brain disease diagnosis and status determination, includes: an image processing unit, which obtains a 3D T1 weighted image, a 2D T2 fluid attenuated inversion recovery (FLAIR) image, a magnetic resonance angiogram (MRA) image, which images only a vessel for checking abnormality of a brain vessel, and a 4D phase-contrast flow image for recognizing a state of a blood flow in a vessel; a complex image analyzing unit, which selects a disease-to-be-diagnosed, sets a brain area according to the selected disease, and analyzes brain tissue and a brain vessel; and a personalized diagnosis and result output unit, which outputs a brain state, a disease-specific risk degree, a risk of disease, and a disease prediction result through a machine learning algorithm by utilizing an age-specific data DB.

Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired. Graphical user interfaces are described for presenting MEG epoch data and a score that correlates to a likelihood of the test individual being cognitively impaired.

The present disclosure processes a user's medical data for each year to be input to a machine learning device for predicting dementia, and a data set composed of optimal features is constructed. The optimal features include at least information on the user's disease history, and the user's medical information for each year in the last 7 years or less. Precise prediction and diagnosis of dementia may be made by constructing the optimal features identified through experiments in the user's medical data for each year. Since the experimental results show that the prediction results of observing a disease history of 7 years or less may be the best, rather than observing medical information for a long period of time, the appropriate criteria may be suggested for predicting dementia.

Techniques for deuterium metabolic imaging (DMI) are provided. According to some aspects, metabolic imaging techniques are described in which deuterium (i.e., 2H)-labeled molecules are detected to assess metabolic processes. These techniques may have an improved spatial resolution compared to conventional techniques, and may provide the ability to determine metabolic information on a voxel-by-voxel basis to form a metabolic image of an imaged volume.

Embodiments of the present systems and methods may relate to a non-invasive system with diagnostic and treatment capacities that use a unified code that is intrinsic to physiological brain function. For example, in an embodiment, a computer-implemented method for affecting living neural tissue may comprise receiving at least one signal from at least one read modality, the signal representing release of photons from mitochondria of the living neural tissue, computing at least one signal to effect alterations to the living neural tissue based on the received input signal, the computed signal adapted to cause transmission of photons to the living neural tissue, and delivering the photons to the living neural tissue to effect alterations to the living tissue.

A cognitive function evaluation device includes: an obtainment unit that obtains use information indicating a state of use of a toilet by a subject; a calculation unit that calculates a total number of times of use of a toilet by the subject, based on the use information obtained by the obtainment unit; an evaluation unit that evaluates a cognitive function of the subject, based on the total number of times of use calculated by the calculation unit; and an output unit that outputs a result of evaluation performed by the evaluation unit.

A computer based method and system forms measurements of brain function and predictions of a likelihood for memory impairment via the use of cameras, including a near-infrared (NIR) spectroscopic device and a camera capturing images in the Red-Green-Blue (RGB) spectrum. The device acquires from a subject measurements of ions, molecules, or combinations thereof at more than one moment in time, as well as amplitude and timing of blood pulsations. Based on the measured molecule or ion concentration, flow, and so forth, a processor assesses the probability of a neuron to form an action potential. Formation of an action potential increases the probability that the subject individual will create a memory.