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Developing an Artificial Intelligence System to Detect Cognitive Impairment

NCT05794451 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 3413

Last updated 2026-05-04

No results posted yet for this study

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

Conditions

Sponsors & Collaborators

  • Technion, Israel Institute of Technology

    collaborator OTHER

  • University of Haifa

    lead OTHER

Principal Investigators

  • Johanna Czamanski-Cohen, PhD · University of Haifa

Eligibility

Min Age
60 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-03-20
Primary Completion
2025-12-31
Completion
2026-03-31

Countries

  • Israel

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05794451 on ClinicalTrials.gov