Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review

Lily Puterman-Salzman; Jory Katz; Howard Bergman; Roland Grad; Vladimir Khanassov; Genevieve Gore; Isabelle Vedel; Machelle Wilchesky; Narges Armanfard; Negar Ghourchian; Samira Abbasgholizadeh Rahimi

doi:10.1159/000533693

Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review

Dement Geriatr Cogn Dis Extra. 2023 Sep 13;13(1):28-38. doi: 10.1159/000533693. eCollection 2023 Jan-Dec.

Authors

Lily Puterman-Salzman¹, Jory Katz¹, Howard Bergman¹, Roland Grad^{1

2}, Vladimir Khanassov¹, Genevieve Gore³, Isabelle Vedel^{1

2}, Machelle Wilchesky^{1

2

4

5}, Narges Armanfard^{6

7}, Negar Ghourchian⁸, Samira Abbasgholizadeh Rahimi^{1

2

6

9}

Affiliations

¹ Department of Family Medicine, McGill University, Montreal, QC, Canada.
² Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada.
³ Schulich Library of Physical Sciences, Life Sciences and Engineering, McGill University, Montreal, QC, Canada.
⁴ Division of Geriatric Medicine, McGill University, Montreal, QC, Canada.
⁵ Donald Berman Maimonides Centre for Research in Aging, Montreal, QC, Canada.
⁶ Mila - Quebec AI Institute, Montreal, QC, Canada.
⁷ Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
⁸ Aerial Technologies Inc., Montreal, QC, Canada.
⁹ Faculty of Dentistry Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada.

Abstract

Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence (AI) may help in detection and screening of dementia; however, little is known in this area.

Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data.

Method: The review followed the framework proposed by O'Malley's and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson's or Huntington's disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened.

Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%).

Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.

Keywords: Artificial intelligence; Dementia; Detection; Machine learning; Motion tracking; Screening.

Publication types

Review

Grants and funding

This study was funded by the Innovation and Partnership program of McGill University and Aerial Tech company (principal investigator: S.A.R.). The authors would like to acknowledge this support. S.A.R. is Canada Research Chair (Tier 2) in Advanced Digital Primary Health Care and received salary support, i.e., Research Scholar Junior 1 Career Development Award, from the Fonds de Recherche du Québec-Santé. S.A.R.’s research program is supported by the Natural Sciences and Engineering Research Council (Discovery Grant No. 2020-05246).