Evidence map›Paper›PMID 38299756›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2024

Validity, feasibility, and effectiveness of a voice-recognition based digital cognitive screener for dementia and mild cognitive impairment in community-dwelling older Chinese adults: A large-scale implementation study.

Xuhao Zhao, Haoxuan Wen, Guohai Xu, Ting Pang, Yaping Zhang, Xindi He, Ruofei Hu, Ming Yan, Christopher Chen, Xifeng Wu and 1 more

Abstract read
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  6. Clinical validation of a voice-based AI tool for screening cognitive impairment: a prospective multicenter study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xuhao ZhaoSchool of Public Health, The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China.
Haoxuan WenSchool of Public Health, The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China.
Guohai XuDAMO Academy, Alibaba Group, Hangzhou, Zhejiang, P. R. China.
Ting PangSchool of Public Health, The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China.
Yaping ZhangSchool of Public Health, The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China.
Xindi HeSchool of Public Health, The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China.
Ruofei HuDAMO Academy, Alibaba Group, Hangzhou, Zhejiang, P. R. China.
Ming YanDAMO Academy, Alibaba Group, Hangzhou, Zhejiang, P. R. China.
Christopher ChenDepartment of Pharmacology, Yong Loo Lin School of Medicine, Memory, Ageing, and Cognition Centre (MACC), National University of Singapore, Singapore, Singapore.
Xifeng WuSchool of Public Health, The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China.
Xin XuSchool of Public Health, The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, Zhejiang, P. R. China.

Funding

Natural Science Foundation of China NSFC/72274170Natural Science Foundation of China NSFC/ 82201733
6 · The paper itself

Abstract

introductionWe investigated the validity, feasibility, and effectiveness of a voice recognition-based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large-scale community of elderly participants.

methodsEligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain-specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale.

resultsAmong the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini-Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). DISCUSSION: This study suggests that the DCS is an effective and efficient tool for dementia and MCI case-finding in large-scale cognitive screening. HIGHLIGHTS: To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community-dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.

Indexed as

Cognitive DysfunctionDementiaAdultAgedChinaCognitionFeasibility StudiesHumansIndependent LivingMiddle AgedNeuropsychological TestsReproducibility of ResultsVoice Recognitioncommunity‐dwelling older adultsdementiadigital cognitive screeningeffectivenessfeasibilitylarge scalemild cognitive impairmentvalidity

Identifiers

PMID38299756
PMCPMC11032546

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.