The present disclosure is directed to systems and methods for measuring and analyzing pupillary psychosensory responses. An electronic device is configured to receive video data with at least two frames. The electronic device then locates one or more eye objects in the video data and determine pupil and iris sizes of the one or more eye objects. The electronic device determines the pupillary psychosensory responses of the one or more eye objects by tracking a ratio of pupil diameter to iris diameter throughout the video. Several metrics for the pupillary psychosensory responses can be determined (e.g., velocity of change of the ratio, peak to peak amplitude of the change in ratio over time, etc.). These metrics can be used as measures of an individual's cognitive ability and mental health in a single session or tracked throughout multiple sessions.

The present invention relates to a cognitive function test server, including a communication interface, a memory; and a processor which is operably connected to the communication interface and the memory, and the processor is configured to provide a first sequence to acquire brainwave data of a user in a resting state by means of an HMD device, acquire baseline brainwave data of the user based on the first sequence, provide at least one second sequence related to a cognitive function by means of the HMD device, acquire input data and activated brainwave data based on the second sequence from the HMD device and an input device connected thereto, and generate a cognitive evaluation result of the user based on at least one of the reference brainwave data, the activated brainwave data, and the input data of the user.

Embodiments may provide self-guided, self-directed diagnostics and treatment of neural conditions. For example, a system may comprise a processor, memory accessible by the processor, and program instructions and data stored in the memory, a plurality of stimulation devices connected to signal output circuitry interfacing the processor with the stimulation devices, program instructions and data to control the stimulation devices to generate and transmit stimulation signals, a plurality of sensing devices connected to signal input circuitry interfacing the processor with the sensing devices, program instructions and data to receive sensed signals from the sensing devices, a communication device adapted to wirelessly communicate with a server computer system, and program instructions and data to perform dynamic closed loop feedback of the stimulation signals based on the received sensed signals to provide self-guided, self-directed diagnostics and treatment of neural conditions using at least one recipe for a treatment strategy guided by artificial intelligence.

A hand-held sized ocular and neurological screening device, system and method, the screening device comprising an eyepiece and a hand-held housing, the housing comprising a tubular stimulus chamber defining a light stimulus channel, wherein an illumination source is configured to provide light stimulus towards an opening through the light stimulus channel and an operational chamber comprising an infrared camera positioned outside the stimulus channel and inclined towards the opening, the infrared camera is configured to capture images of the pupils and eye movements through the opening without interfering with the light stimulus and a controller configured to receive the captured images from the infrared camera. The hand-held sized device can include a clip-on fixture for fixing the device onto a table, a desktop, or any portable ophthalmic apparatus.

Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.

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.

An on-person portable device and/or app that allows for constant location monitoring of dementia patients is disclosed. Such a device and/or app also includes automated communication of facial recognition of persons proximally near such a patient as well as possible generation of certain music in response to patient status and/or location. The patient is provided the device and/or app for handling or wearing, such as a phone or pair of glasses, in order to follow location through GPS monitoring, and a reactive program therein that automatically views another person and provides an identification thereof coupled with a voice modulator within an earpiece or like device for memory stimulation pertaining to loved ones or friends, and the like. The same program may also provide the music response to comments, movement, or any other external stimuli to the subject patient, again, to evoke a memory stimulus, as well.

A method of quantitatively evaluating a cognitive impairment 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 impairment and predict its future change based on the at least one feature obtained.

Arrangements and methods are provided for obtaining information associated with an anatomical sample. For example, at least one first electro-magnetic radiation can be provided to the anatomical sample so as to generate at least one acoustic wave in the anatomical sample. At least one second electro-magnetic radiation can be produced based on the acoustic wave. At least one portion of at least one second electro-magnetic radiation can be provided so as to determine information associated with at least one portion of the anatomical sample. In addition, the information based on data associated with the second electro-magnetic radiation can be analyzed. The first electro-magnetic radiation may include at least one first magnitude and at least one first frequency. The second electro-magnetic radiation can include at least one second magnitude and at least one second frequency. The data may relate to a first difference between the first and second magnitudes and/or a second difference between the first and second frequencies. The second difference may be approximately between −100 GHz and 100 GHz, excluding zero.

A method and device for predicting Alzheimer's disease based on voice characteristics are provided. The device for predicting Alzheimer's disease according to an embodiment includes: a voice input unit configured to generate a voice sample by recording a voice of a subject; a data input unit configured to receive demographic information of the subject; a voice characteristic extraction unit configured to extract voice characteristics from the generated voice sample; and a prediction model that is pre-trained to predict presence or absence of Alzheimer's disease in the subject, based on the voice characteristics and the demographic information.