Evidence map›Paper›PMID 39410879›Full record

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

Artificial intelligence-assisted oculo-gait measurements for cognitive impairment in cerebral small vessel disease.

Huimin Chen, Hao Du, Fang Yi, Tingting Wang, Shuo Yang, Yuesong Pan, Hongyi Yan, Dandan Liu, Mengyuan Zhou, Yiyi Chen and 10 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 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–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

12 citing papers in PubMed.

  1. Article
  2. Review
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  4. [Correlation between eye movement and white matter hyperintensity characteristics in patients with cerebral small vessel disease].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 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

20 authors.

Huimin ChenDepartment of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Dongcheng, Beijing, China.
Hao DuDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Fang YiDepartment of Geriatric Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Tingting WangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.
Shuo YangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.
Yuesong PanDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.
Hongyi YanChina National Clinical Research Center for Neurological Diseases, Fengtai, Beijing, China.
Dandan LiuDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.
Mengyuan ZhouDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.
Yiyi ChenDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.
Mengxi ZhaoDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.
Jingtao PiBeijing Tsinghua Changgung Hospital, Changping, Beijing, China.
Yingying YangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.
Xiangmin FanInstitute of Software, Chinese Academy of Sciences, Beijing, China.
Xueli CaiDepartment of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui, Zhejiang, China.
Ziyu QiuHealth Service Department of the Guard Bureau of the General Office of the Central Committee of the Communist Party of China, Xicheng, Beijing, China.
Jipeng ZhangHealth Service Department of the Guard Bureau of the General Office of the Central Committee of the Communist Party of China, Xicheng, Beijing, China.
Yawei LiuHealth Service Department of the Guard Bureau of the General Office of the Central Committee of the Communist Party of China, Xicheng, Beijing, China.
Wenping GuDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Yilong WangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, Fengtai, Beijing, China.

Funding

Beijing Laboratory of Oral Health PXM2021_014226_000041Beijing Outstanding Young Scientist Program BJJWZYJH01201910025030Beijing Talent, Project - Class A: Innovation and Development 2018A12Capital Funds for Health Improvement and Research 2022-2-2045Key R&D Plan of Hunan Province 2023SK2019National Healthcare Research Funding 2022YB05National High Level Hospital Clinical Research Funding BJ-2024-183National Key R&D Program of China 2017YFC1307900National Key R&D Program of China 2017YFC1307905National Key R&D Program of China 2022YFF1501500National Key R&D Program of China 2022YFF1501501National Key R&D Program of China 2022YFF1501502National Key R&D Program of China 2022YFF1501503National Key R&D Program of China 2022YFF1501504National Key R&D Program of China 2022YFF1501505National Natural Science Foundation of China 81825007National Natural Science Foundation of China 82202085National Ten-Thousand Talent PlanYouth Beijing Scholar Program 010
6 · The paper itself

Abstract

introductionOculomotor and gait dysfunctions are closely associated with cognition. However, oculo-gait patterns and their correlation with cognition in cerebral small vessel disease (CSVD) remain unclear.

methodsPatients with CSVD from a hospital-based cohort (n = 194) and individuals with presumed early CSVD from a community-based cohort (n = 319) were included. Oculo-gait patterns were measured using the artificial intelligence (AI) -assisted 'EyeKnow' eye-tracking and 'ReadyGo' motor evaluation systems. Multivariable linear and logistic regression models were employed to investigate the association between the oculo-gait parameters and cognition.

resultsAnti-saccade accuracy, stride velocity, and swing velocity were significantly associated with cognition in both patients and community dwellers with CSVD, and could identify cognitive impairment in CSVD with moderate accuracy (area under the curve [AUC]: hospital cohort, 0.787; community cohort, 0.810) after adjusting for age and education. DISCUSSION: The evaluation of oculo-gait features (anti-saccade accuracy, stride velocity, and swing velocity) may help screen cognitive impairment in CSVD. HIGHLIGHTS: Oculo-gait features (lower anti-saccade accuracy, stride velocity, and swing velocity) were associated with cognitive impairment in cerebral small vessel disease (CSVD). Logistic model integrating the oculo-gait features, age, and education level moderately distinguished cognitive status in CSVD. Artificial intelligence-assisted oculomotor and gait measurements provide quick and accurate evaluation in hospital and community settings.

Indexed as

Artificial IntelligenceCerebral Small Vessel DiseasesCognitive DysfunctionAgedCohort StudiesFemaleGaitHumansMaleMiddle AgedNeuropsychological Testsartificial intelligencecerebral small vessel diseasecognitioneye trackinggait

Identifiers

PMID39410879
PMCPMC11667535

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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.