A method for facilitating a Parkinson's Disease (PD) assessment of a patient includes capturing first video of a patient performing first test movements while holding the mobile device; capturing second video of the patient performing second test movements while maintaining the mobile device on their person; capturing third video of the patient performing third test movements including standing and walking; capturing one or more IMU readings using an IMU of the mobile device; processing the first video, the second video, and the third video according to (i) a hand landmark model to generate one or more hand biomarkers, (ii) a face landmark model to generate one or more face biomarkers, and (iii) a body landmark model to generate one or more body biomarkers; and determining an assessment score based on a standardized PD assessment by processing the biomarkers.

A processor is configured to specify a first section and a second section in which a detection target of movement of a subject is different, based on a measurement value of the movement of the subject measured by a device that is mountable on the subject; acquire at least one feature amount representing a feature of a disease related to at least one of cognition or motion based on each of a measurement value of the first section and a measurement value of the second section; and acquire a determination result of the disease based on the feature amount and a predetermined determination criterion.

An auxiliary diagnosis method for Parkinson's disease (PD) based on static and dynamic features of facial expressions is provided. Video data of various facial expressions performed by a to-be-tested patient is acquired and pre-processed to extract a plurality of optimal facial expression images corresponding to the various facial expressions. A similarity discrimination is performed on a synthesized happy facial expression image of the to-be-tested patient in a healthy state and an extracted happy facial expression image to obtain similarity features. Distances between multiple facial key points in the various facial expression images are calculated to obtain multiple key features, which are spliced with the plurality of key features to form static features. Coordinate change degrees of multiple facial key points of eyelids and mouth are calculated to obtain dynamic features. A classification prediction result of PD is output based on the spliced features.

A system, method and computer program for assessment of body movement's conditions or disorders are proposed. The system comprises several monitoring sensors to be attached to different body areas of a person to obtain biomechanical and/or physiological variables thereof according to a specified sensor configuration; a memory or database, having stored therein at least one of: control biomechanical and/or physiological variables and pathology biomechanical and/or physiological variables obtained. The variables being stored classified in three different categories: cardiac, kinematics and plantar-pressure. A processing unit generating a normality model; getting biomechanical and/or physiological variables from a given user, classifying them into the three different categories, and selecting, for each category, those that are significant; computing, for the selected variables of the given user, a unified category score for each category; and computing a condition or disorder score as the deviation between the computed unified category scores with the generated normality model.

An auxiliary diagnosis method for Parkinson's disease (PD) based on static and dynamic features of facial expressions is provided. Video data of various facial expressions performed by a to-be-tested patient is acquired and pre-processed to extract a plurality of optimal facial expression images corresponding to the various facial expressions. A similarity discrimination is performed on a synthesized happy facial expression image of the to-be-tested patient in a healthy state and an extracted happy facial expression image to obtain similarity features. Distances between multiple facial key points in the various facial expression images are calculated to obtain multiple key features, which are spliced with the plurality of key features to form static features. Coordinate change degrees of multiple facial key points of eyelids and mouth are calculated to obtain dynamic features. A classification prediction result of PD is output based on the spliced features.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Described herein are techniques for identifying regions of substantia nigra reticulata (SNR) and regions of substantia nigra compact dorsal (SNCD) in histology images and quantifying a number of dopaminergic neural cells within the images. In some embodiments, an image of a section of a brain may be input into a first machine learning model to obtain a first segmentation map comprising pixel-wise labels indicative of whether a corresponding pixel in the image depicts a region of SNR or SNCD. In some embodiments, the image (and, optionally, the first segmentation map) may also be input to a second machine learning model trained to generate a second segmentation map comprising pixel-wise labels indicating whether a corresponding pixel of the image depicts a dopaminergic neural cell or neural background tissue. The number of cells within the image may be determined based on the second segmentation map.

A portable device (1) for quantifying movements of pronation and/or supination of a person, and intended to be used when the person has the elbow on a horizontal support, comprising: a solid armature (2) with: means for attaching (4), configured to secure one hand of the person in the device (1); a central pivot element (5) intended to be in contact with the horizontal support, during the angular oscillations of the hand, between two opposed identical first block angles relative to a vertical position called neutral position, the first block angles defining a small amplitude, first means for blocking (8) the movement of pronation and the movement of supination of the elbow, at the two first block angles; second means for blocking (9) the movement of pronation and the movement of supination, at two identical opposed second block angles relative to the neutral position, between the solid armature (2) relative to the horizontal support, the second block angles defining a large amplitude greater than the small amplitude; an IMU (3) able to measure movement data.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.