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Developing an Artificial Intelligence System to Detect Cognitive Impairment

Developing an Artificial Intelligence System to Detect Mild Cognitive Impairment and Alzheimer's Disease Dementia Through Self-Figure Drawing: An Innovative Approach

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05794451
Enrollment
3413
Registered
2023-04-03
Start date
2023-03-20
Completion date
2026-03-31
Last updated
2026-05-04

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Alzheimer Disease, Healthy Aging, Mild Cognitive Impairment

Brief summary

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

Interventions

None listed

Sponsors

University of Haifa
Lead SponsorOTHER
Technion, Israel Institute of Technology
CollaboratorOTHER

Study design

Observational model
COHORT
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Age
60 Years to No maximum
Healthy volunteers
Yes

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).

Design outcomes

Primary

MeasureTime frameDescription
CognitionOne dayThe Montreal Cognitive Assessment (MoCA) is a 10-minute paper-based test that aims to detect MCI in older patients with symptomatology, suggesting impaired cognition. The MoCA is composed of 12 tasks to detect short-term memory, visuospatial ability, executive functioning, phonemic fluency, abstraction, attention, concentration, working memory, language, and orientation.
Cognition for adults diagnosed with Alzheimer's diseaseOne dayThe Self-reported Cognitive Difficulties (CDS)75 is a 39-item questionnaire that requires participants or their caregivers in case of AD to rate how often they currently experience cognitive difficulties in everyday life using a 5-point scale (0 -"never" to 4 -"very often").
Self-figure drawing -CognitionOne daySelf-figure drawing. Participants will be asked to draw themselves using a pencil on an A4-sized sheet of paper.

Countries

Israel

Contacts

PRINCIPAL_INVESTIGATORJohanna Czamanski-Cohen, PhD

University of Haifa

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: May 5, 2026