PENSIEVE-AI a brief cognitive test to detect cognitive impairment across diverse literacy

Undiagnosed cognitive impairment is a pervasive global issue, often due to subtle nature of early symptoms, necessitating the use of brief cognitive tests for early detection. However, most brief tests are not scalable (requiring trained professionals), and are not designed for lower literacy groups (e.g. in underserved communities). Here, we developed PENSIEVE-AI<sup>TM</sup>, a drawing-based digital test that is less dependent on literacy, and can be self-administered in <5 min. In a prospective study involving 1758 community-dwelling individuals aged 65 and older from Singapore (education range = 0-23 years), our deep-learning model showed excellent performance in detecting clinically-adjudicated mild cognitive impairment and dementia (AUC = 93%), comparable to traditional neuropsychological assessments (AUC = 94%, P<sub>comparison</sub> = 1.000). Results were consistent even across education subgroups. Being less dependent on literacy, PENSIEVE-AI holds promise for broader deployment in literacy-diverse populations similar to Singapore (e.g. some Asian and lower- and middle-income countries), potentially improving early detection and intervention of cognitive impairment.

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Publication Details

Journal
Nature Communications
Published
2025-03-23
DOI
https://doi.org/10.1038/s41467-025-58201-x
Citations
6
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
8.83
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article

PENSIEVE-AI a brief cognitive test to detect cognitive impairment across diverse literacy

Weishan Li, Jong Yong Abdiel Foo, Congyuan Tan, Julian Thumboo et al.
6 citations
Nature Communications
Dementia and Cognitive Impairment Research
8.83
article

PENSIEVE-AI a brief cognitive test to detect cognitive impairment across diverse literacy

Weishan Li, Jong Yong Abdiel Foo, Congyuan Tan, Julian Thumboo, Calvin Sia, Tau Ming Liew, Gerald Choon‐Huat Koh, Howard H. Yang, Rebecca Chong, Way Inn Koay, Kaavya Narasimhalu, Simon Kang Seng Ting, Dan Luo, King Fan Yip, Sze Yan Tay, Rachel Shong
article en
6 citations

Abstract

Undiagnosed cognitive impairment is a pervasive global issue, often due to subtle nature of early symptoms, necessitating the use of brief cognitive tests for early detection. However, most brief tests are not scalable (requiring trained professionals), and are not designed for lower literacy groups (e.g. in underserved communities). Here, we developed PENSIEVE-AI<sup>TM</sup>, a drawing-based digital test that is less dependent on literacy, and can be self-administered in <5 min. In a prospective study involving 1758 community-dwelling individuals aged 65 and older from Singapore (education range = 0-23 years), our deep-learning model showed excellent performance in detecting clinically-adjudicated mild cognitive impairment and dementia (AUC = 93%), comparable to traditional neuropsychological assessments (AUC = 94%, P<sub>comparison</sub> = 1.000). Results were consistent even across education subgroups. Being less dependent on literacy, PENSIEVE-AI holds promise for broader deployment in literacy-diverse populations similar to Singapore (e.g. some Asian and lower- and middle-income countries), potentially improving early detection and intervention of cognitive impairment.

Nature CommunicationsVol. 16(1)
SingHealth (SG), National University of Singapore (SG), Singapore General Hospital (SG), Defence Science and Technology Agency (SG), SingHealth Duke-NUS Academic Medical Centre (SG), Université Bourgogne Franche-Comté (FR), Duke-NUS Medical School (SG), National Neuroscience Institute (SG), Agency for Integrated Care (SG)
Quality Education
Openalex Percentile: Top 2%
Dementia and Cognitive Impairment Research
8.83
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