Evidence map›Paper›PMID 33123214›Full record

ArticleBiomedical signal processing and control2021

Detection of Mild Cognitive Impairment and Alzheimer's Disease using Dual-task Gait Assessments and Machine Learning.

Behnaz Ghoraani, Lillian N Boettcher, Murtadha D Hssayeni, Amie Rosenfeld, Magdalena I Tolea, James E Galvin

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PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical signal processing and control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 3 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

52 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Gait in cerebral small vessel disease, pre-dementia, and dementia: A systematic review.International journal of stroke : official journal of the International Stroke Society · 2023
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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

6 authors.

Behnaz GhoraaniDepartment of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 US.
Lillian N BoettcherDepartment of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 US.
Murtadha D HssayeniDepartment of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 US.
Amie RosenfeldComprehensive Center for Brain Health, Department of Neurology, University of Miami, Miami, FL 33136 US.
Magdalena I ToleaComprehensive Center for Brain Health, Department of Neurology, University of Miami, Miami, FL 33136 US.
James E GalvinComprehensive Center for Brain Health, Department of Neurology, University of Miami, Miami, FL 33136 US.

Funding

Multicultural Community Dementia ScreeningR01AG071514 · NIA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI GALVIN, JAMES E · 2021 to 2025
$13.8M
Reducing Disparities in Dementia and VCID Outcomes in a Multicultural Rural PopulationR01NS101483 · NINDS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI GALVIN, JAMES E · 2019 to 2023
$8.0M
Multicultural Community Dementia Screening.R01AG040211 · NIA · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI GALVIN, JAMES E · 2011 to 2018
$3.2M
NIA NIH HHS R01 AG040211NIA NIH HHS R01 AG071514NINDS NIH HHS R01 NS101483
6 · The paper itself

Abstract

objectiveEarly detection of mild cognitive impairment (MCI) and Alzheimer's disease (AD) can increase access to treatment and assist in advance care planning. However, the development of a diagnostic system that d7oes not heavily depend on cognitive testing is a major challenge. We describe a diagnostic algorithm based solely on gait and machine learning to detect MCI and AD from healthy.

methodsWe collected "single-tasking" gait (walking) and "dual-tasking" gait (walking with cognitive tasks) from 32 healthy, 26 MCI, and 20 AD participants using a computerized walkway. Each participant was assessed with the Montreal Cognitive Assessment (MoCA). A set of gait features (e.g., mean, variance and asymmetry) were extracted. Significant features for three classifications of MCI/healthy, AD/healthy, and AD/MCI were identified. A support vector machine model in a one-vs.-one manner was trained for each classification, and the majority vote of the three models was assigned as healthy, MCI, or AD.

resultsThe average classification accuracy of 5-fold cross-validation using only the gait features was 78% (77% F1-score), which was plausible when compared with the MoCA score with 83% accuracy (84% F1-score). The performance of healthy vs. MCI or AD was 86% (88% F1-score), which was comparable to 88% accuracy (90% F1-score) with MoCA.

conclusionOur results indicate the potential of machine learning and gait assessments as objective cognitive screening and diagnostic tools. SIGNIFICANCE: Gait-based cognitive screening can be easily adapted into clinical settings and may lead to early identification of cognitive impairment, so that early intervention strategies can be initiated.

Indexed as

Alzheimer’s diseaseCognitive declinedual-task assessmentgait datamachine learning

Identifiers

PMID33123214
PMCPMC7591132

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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.