Online ISSN: 1945-4589

Aging-US: Screening Alzheimer’s disease by facial complexion using artificial intelligence

02-02-2021

Aging-US published "Screening of Alzheimer’s disease by facial complexion using artificial intelligence" which reported that despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia is yet to be discovered.

This study aimed to examine whether artificial intelligence could distinguish between the faces of people with cognitive impairment and those without dementia.

121 patients with cognitive impairment and 117 cognitively sound participants were recruited for the study.

The binary differentiation of dementia / non-dementia facial image was expressed as a “Face AI score”.

However, MMSE score showed significantly stronger correlation with Face AI score than chronological age.

This Aging-US study showed that deep learning programs such as Xception have the ability to differentiate the faces of patients with mild dementia from that of patients without dementia, paving the way for future studies into the development of a facial biomarker for dementia.

This Aging-US study showed that deep learning programs such as Xception have the ability to differentiate the faces of patients with mild dementia from that of patients without dementia.

Dr. Masashi Kameyama from The University of Tokyo said, "Dementia is one of the most serious problems facing a global aging population."

It was demonstrated that perceived age reflects cognitive function more closely than chronological age.

Thus, it was postulated that cognitive decline may be expressed in a patient’s face.

Based on this information, it was hypothesized that AI software may be able to classify patients as having cognitive impairment or not using facial recognition.

Figure 3. Association of Face AI score with (A) MMSE and (B) chronological age. Face AI score correlated closely with (a) MMSE (r = −0.599, t = −16.40, p = 2.47×10−48) and relatively weakly with (b) age (r = 0.321, t = 7.44, p = 4.57 × 10−13). The Steiger’s test found the difference in correlation coefficients to be significant (p = 3.25 × 10−35).

The present study aimed to examine whether AI can distinguish facial traits of cognitive impairment patients from that of non-dementia patients.

The findings of this study lay the foundations for the development of a non-invasive, inexpensive and rapid screening tool for cognitive impairment using AI.

The Kameyama Research Team concluded in their Aging-US Research Paper, "The study showed that deep learning software such as Xception has the ability to differentiate facial images of people with mild dementia from those of people without dementia. This may pave the way for the clinical use of facial images as a biomarker of dementia."

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DOI - https://doi.org/10.18632/aging.202545

Full Text - https://www.aging-us.com/article/202545/text

Correspondence to: Masashi Kameyama email: kame-tky@umin.ac.jp

Keywords: artificial intelligence, face, dementia, machine learning

About Aging-US:

Aging publishes research papers in all fields of aging research including but not limited, aging from yeast to mammals, cellular senescence, age-related diseases such as cancer and Alzheimer’s diseases and their prevention and treatment, anti-aging strategies and drug development and especially the role of signal transduction pathways such as mTOR in aging and potential approaches to modulate these signaling pathways to extend lifespan. The journal aims to promote treatment of age-related diseases by slowing down aging, validation of anti-aging drugs by treating age-related diseases, prevention of cancer by inhibiting aging. Cancer and COVID-19 are age-related diseases.

Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).

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