A system and method for collecting symptomatic data to screen for a targeted disease. Testing hardware incorporates a plurality of testing units with corresponding indicators that can be altered to indicate whether a symptom is present or not. The resulting data from the testing use can then be analyzed to determine the likelihood of presence of a disease.

Machine learning-based systems and platforms use digital data to process data sets to generate assessments including classifications and/or regressions.

A telemedicine system with dynamic imaging is disclosed herein. In some embodiments, the telemedicine system comprises a laser imaging and treatment apparatus, and associated systems and methods that allow a physician (e.g., a surgeon) to perform laser surgical procedures on an eye structure or a body surface with a laser imaging and treatment apparatus disposed at a first (i.e. local) location from a control system disposed at a second (i.e. remote) location, e.g., a physician's office. Also, in some embodiments, communication between the laser imaging and treatment apparatus and control system is achieved via the Internet®. Further, in some embodiments, the telemedicine system includes a dynamic imaging system and/or a facial recognition system that verifies the identity of a patient, and is capable of being used for other important applications, such as tracking and analyzing trends in a disease process.

The present disclosure relates to a method and a server capable of performing a dementia test without the need for a dementia test subject to personally visit a test center and capable of automatically performing a dementia test by an artificial intelligence, not an individual. According to an embodiment of the present disclosure, a dementia test may be performed without the need for a dementia test subject to personally visit a test center, thereby improving the effectiveness of a dementia test by filtering patients who need to visit the test center and proceed with a follow-up test, and a dementia test may be automatically performed by an artificial intelligence, not an individual, thereby saving the time required for the dementia test and costs such as labor cost for the test.

The present invention describes a method for the detection, diagnosis and monitoring of cognitive impairment and Alzheimer's disease. The method involves the detection of pericyte loss and deficiency in PDGFRβ, alone or in combination with retinal vascular Aβ deposits. The method also involves detecting an alteration in blood-retinal barrier (BRB) cell tight junction.

A method for analyzing dementia severity, may comprise calculating color data from skin regions of interest of a frontal lobe region; calculating frequency values corresponding to a frequency band of a dementia-related biosignal based on the calculated color data; and analyzing dementia severity using the calculated frequency values.

This invention relates to disease detection by imaging and treatment of virus infection. Previously, there was no way to use CEST MRI imaging to early detect and map the neurodegenerative diseases, multiple sclerosis disease, concussion, and traumatic brain injury. Also, previously, there was no way to use Computed Tomography (CT) imaging to early detect and map the neurodegenerative diseases. Embodiments of the present invention use a non-invasive CEST MRI imaging method is disclosed for early detection of diseases by using MRI or by using CT. The endogenous (MRI) contrast of the biological tissue can rely on the endogenous protons of the proteins and peptides as a source of the contrast, such as hydroxyl, amine, and amide protons, and thereby provide imaging and mapping for the early detection of the neurodegenerative diseases, multiple sclerosis disease, concussion, traumatic brain injury, and other diseases by using endogenous protons contrast via CEST MRI. Also, the exogenous agents can be used to produce MRI contrast, such as agents contain exchangeable protons and thereby provide imaging and mapping the inflammation in cancer and the expressed proteins in cancer cells for cancer detection. Also, using exogenous CT contrast agents for detection of amyloid beta, tau protein, alpha-synuclein protein, and aggregation proteins in neurodegenerative diseases and inflammation in many diseases such as neurodegenerative diseases, cancer and other inflammatory diseases Also, this invention relates to novel methods of treatment virus infection and enhance the immune system to produce antibodies against the viruses.

The present invention provides a system for detecting whether a subject having a target suffers from an Alzheimer's disease. The system includes a multi-harmonic generation microscope and a processor. The multi-harmonic generation microscope images the target by a second harmonic generation (SHG) and a third harmonic generation (THG) to respectively obtain an SHG image and a THG image. The processor couples to the multi-harmonic generation microscope and configures to add a first color to the SHG image and a second color to the THG image to respectively obtain a color-added SHG image and a color-added THG image, and combine the color-added SHG image and the color-added THG image to obtain a combined image, wherein the combined image is used to determine whether the subject suffers from the Alzheimer's disease.

Disclosed are a dementia test device and dementia diagnosis device, and a dementia diagnosis system comprising same. Provided according to one aspect of the present embodiment are a dementia test device, dementia diagnosis device, and dementia diagnosis system which can accurately diagnose the possibility of dementia in a patient by analyzing the concentration of hemoglobin.

Provided is a method for classifying severe cognitive impairment patients by analyzing electroencephalogram (EEG) data. The method of classifying severe cognitive impairment patients by analyzing EEG data includes a brainwave collection step of collecting EEG data on a plurality of users, a first classification step of classifying the plurality of users into a severe cognitive impairment group or a non-severe cognitive impairment group by analyzing the collected EEG data, a second classification step of classifying users included in the non-severe cognitive impairment group into a normal group or an amnestic mild cognitive impairment (aMCI) group, and a third classification group of classifying users included in the normal group into a within normal limits (WNL) group or a preclinical Alzheimer's disease (AD) group and classifying users included in the aMCI group into a non-AD MCI group or a prodromal AD group.