Enhancing Dementia Classification for Diverse Demographic Groups: Using Vision Transformer-Based Continuous Scoring of Clock Drawing Tests.
Objectives Alzheimer's disease and related dementias significantly affect older adults' quality of life. The clock-drawing test (CDT) is a widely used dementia screening tool due to its ease of administration and effectiveness. However, manual CDT-coding in large-scale studies can be time-intensive...
| Publicado en: | Journals of Gerontology Series B: Psychological Sciences & Social Sciences Vol. 80; no. 7; pp. 1 - 8 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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Oxford University Press / USA
Jul2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=189082038&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 189082038 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10795014 JGB jtl: Journals of Gerontology Series B: Psychological Sciences & Social Sciences issn: 10795014 maglogo: N pubinfo: dt: Jul2025 vid: 80 iid: 7 pid: 622 pub: Oxford University Press / USA artinfo: ui: 189082038 10.1093/geronb/gbaf065 ppf: 1 ppct: 7 formats: tig: atl: Enhancing Dementia Classification for Diverse Demographic Groups: Using Vision Transformer-Based Continuous Scoring of Clock Drawing Tests. aug: au: Hu, Mengyao Murphey, Yi Lu Qin, Tian Melipillán, Edmundo R Zahodne, Laura B Gonzalez, Richard Freedman, Vicki A affil: Management, Policy, and Community Health, School of Public Health, the University of Texas Health Science Center at Houston, Houston, Texas, USA Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA College of Engineering and Computer Science, University of Michigan–Dearborn, Dearborn, Michigan, USA Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA su: United States Health services accessibility African Americans Psychometrics Health equity Educational attainment Alzheimer's disease diagnosis Cognition disorders diagnosis Predictive tests Alzheimer's disease Receiver operating characteristic curves Statistical significance Research funding Research evaluation Descriptive statistics Routine diagnostic tests Deep learning Artificial neural networks Neuropsychological tests Research methodology Data analysis software Algorithms Sensitivity & specificity (Statistics) sug: subj: Health services accessibility African Americans Psychometrics Health equity Educational attainment United States Alzheimer's disease diagnosis Cognition disorders diagnosis Predictive tests Alzheimer's disease Receiver operating characteristic curves Statistical significance Research funding Research evaluation Descriptive statistics Routine diagnostic tests Deep learning Artificial neural networks Neuropsychological tests Research methodology Data analysis software Algorithms Sensitivity & specificity (Statistics) keyword: Cognitive function Deep learning neural networks Machine learning Cognitive function Deep learning neural networks Machine learning ab: Objectives Alzheimer's disease and related dementias significantly affect older adults' quality of life. The clock-drawing test (CDT) is a widely used dementia screening tool due to its ease of administration and effectiveness. However, manual CDT-coding in large-scale studies can be time-intensive and prone to coding errors and is typically limited to ordinal responses. In this study, we developed a continuous CDT score using a deep learning neural network (DLNN) and evaluated its ability to classify participants as having dementia or not. Methods Using a nationally representative sample of older adults from the National Health and Aging Trends Study (NHATS), we trained deep learning models on CDT images to generate both ordinal and continuous scores. Using a modified NHATS dementia classification algorithm as a benchmark, we computed the area under the receiver operating characteristic curve for each scoring approach. Thresholds were determined by balancing sensitivity and specificity, and demographic-specific thresholds were compared to a uniform threshold for classification accuracy. Results Continuous CDT scores provided more granular thresholds than ordinal scores for dementia classification, which vary by demographic characteristics. Lower thresholds were identified for Black individuals, those with lower education, and those ages 90 or older. Compared to ordinal scores, continuous scores also allowed for a more balanced sensitivity and specificity. Discussion This study demonstrates the potential of continuous CDT generated by DLNN to enhance dementia classification. By identifying demographic-specific thresholds, it offers a more inclusive and adaptive approach, which could lead to improved guidelines for using CDT in dementia screening. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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