A mathematical model may be trained to diagnose a medical condition of a person by processing acoustic features and language features of speech of the person. The performance of the mathematical model may be improved by appropriately selecting the features to be used with the mathematical model. Features may be selected by computing a feature selection score for each acoustic feature and each language feature, and then selecting features using the scores, such as by selecting features with the highest scores. In some implementations, stability determinations may be computed for each feature and features may be selected using both the feature selection scores and the stability determinations. A mathematical model may then be trained using the selected features and deployed. In some implementations, prompts may be selected using computed prompt selection scores, and the deployed mathematical model may be used with the selected prompts.

Apparatuses, systems, methods, and computer program products are disclosed for medical assessment based on voice. A query module is configured to audibly question a user from an electronic display screen and/or a speaker of a computing device with one or more open ended questions. A response module is configured to receive a conversational verbal response of a user from a microphone of a computing device in response to one or more open ended questions. A detection module is configured to provide a machine learning assessment for a user of a medical condition based on a machine learning analysis of a received conversational verbal response of the user.

A system and method for a therapeutic apparatus that includes an external casing of a first therapeutic apparatus, wherein the external casing comprises of a plush body, the external casing comprising at least one compartment; a therapeutic module system that comprises of a set of interaction devices and a wireless communication system, wherein the therapeutic module system may be removably oriented into the at least one compartment; the set of interaction devices comprising of: sensing inputs that comprise of a auditory sensing system, a haptic sensing system, an olfaction sensing system, and other biosensing systems, and output systems that comprise of at least an auditory feedback and haptic feedback systems; and a processing system that comprises configuration to assess the state of a subject through the sensing inputs and control the output systems in response to the sensing inputs.

Provided are a disease prediction apparatus and a disease prediction method using the same, which may easily learn a biosignal, may easily make a diagnosis, and may perform analysis in real time, in order to determine a disease using a biosignal via deep learning.

Systems and methods for determining a predicted brain health of a first user and providing individualized health recommendations to the first user based at least in part on an artificial neural network trained on previously existing anatomical and functional neuroimaging information, behavioral information, self-reported information, and other types of information of a plurality of users. The artificial neural network may be capable of determining a predicted benchmark of brain health of the first user's information when compared with the previously existing users' information and storing the first user's anatomical and/or functional neuroimaging information, behavioral information, self-reported information, and other types of information in a database such that the artificial neural network can be re-trained based on the new information of the first user. The first user can be notified of his or her brain health and receive individualized health recommendations.

Preferred embodiments of the present invention provide an apparatus and method for providing dementia diagnosis assistance information. In the present invention, an MRI brain image and a PET brain image of a subject for diagnosis are received, the MRI brain image is divided into a plurality of regions and registered with the PET brain image. Then, a standardized uptake value ratio (SUVR) of each divided region is obtained from the registered image, and for each divided region, a standard value indicating the degree of proximity of a SUVR of the subject to the average value of the SUVR of Alzheimer's group and the average value of the SUVR of the normal group is obtained. Then, the standard value of the SUVR of the subject is displayed together with the average value and the range of the standard deviation of each control group for each of the divided regions as a graph.

A method and system for generating a classifier to classify facial images for cognitive disorder in humans. The system comprises receiving a labeled dataset including set of facial images, wherein each of the facial image is labeled depending on whether it represents a cognitive disorder condition; extracting, from each facial image in the set of facial images, at least one learning facial feature indicative of a cognitive disorder; and feeding the extracted facial features into a to produce a machine learning trained model to generate a classifier, wherein the classifier is generated and ready when the trained model includes enough facial features processed by a machine learning model.

The present invention provides systems and methods for assessing Alzheimer’s disease using Fractal Dimension Distributions (FDD).

A method of diagnosing Alzheimer's disease using a positron emission tomography-computed tomography (PET-CT) image may include generating a standard brain CT template in a Montreal Neurological Institute (MNI) region based on a CT image calculated from a PET-CT apparatus, calculating a whole cortex volume of interest (VOI) for a plurality of detail regions capable of being used in .sup.18F-florbetaben (FBB) and .sup.18F-flutemetamol (FMM) in common within a cortex ROI region in which a deposition of beta amyloid protein is equal to or higher than a given value based on the standard brain CT template, and calculating a centiloid of each of the plurality of detail regions based on an amyloid standardized uptake value ratio (SUVR) of each of the plurality of detail regions.

A system and method for evaluation, detection, conditioning, and treatment of neurological functioning and conditions which uses data obtained while a person is engaged in simultaneously in a range of primary physical tasks combined with defined types of secondary activity, such as listening, reading, speaking, mathematics, logic puzzles, navigation of a virtual environment, recall of past stimuli, etc. The data from the physical and secondary activities are combined to generate a composite functioning score visualization indicating the relative functioning of areas aspects of neurological functioning; including those in which deficiencies may be present, which are early indicators of possible neurological conditions. Through algorithmic recommendations combined with expert and user input, a conditioning regimen targeting neurological aspects of interest paired with periodic testing allows the user to track their progress in these areas over time.