Developing an Artificial Intelligence System to Detect Cognitive Impairment

University of Haifa

Status

Enrolling

Conditions

Alzheimer Disease

Mild Cognitive Impairment

Healthy Aging

Study type

Observational

Funder types

Other

Identifiers

NCT05794451

ALZAI

Details and patient eligibility

About

Alzheimer's disease dementia (AD) is a debilitating and prevalent neurodegenerative disease in older adults globally. Cognitive impairment, a hallmark of AD, is assessed through verbal tests that require high specialization, and while accepted as screening tools for AD, general practitioners seldom use them. AD can be diagnosed with expensive, invasive neuroimaging and blood tests, but these are usually conducted when cognitive functioning is already severely impaired. Thus, finding a novel, non-invasive tool to detect and differentiate mild cognitive impairment (MCI) and AD is a prime public health interest. Self-figure drawings (a projective tool in which individuals are asked to draw a picture of themselves), are easy to administer and have been shown to differentiate between healthy and cognitively impaired individuals, including AD. Convolutional Neural Network (CNN) (a type of deep neural network, applied to analyze visual imagery) has advanced to assess health conditions using art products. Therefore, the proposed study suggests utilizing CNN-based methods to develop and test an application tailored to differentiate between drawings of individuals with MCI, AD, and healthy controls (HC) using 4,000 self-figure drawings. This

Enrollment

4,000 estimated patients

Sex

All

Ages

60+ years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria

Adults aged 60 and above with subtle signs of risk of future cognitive decline, residing in the community or in nursing homes with a minimum of 10 years of education.

Exclusion criteria

Current or past psychiatric illness, the presence of congenital/organic cognitive condition, severe visual or motor impairment, and terminal illness (to avoid the effect of comorbidities).