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Abstract IntroductionAutomated computational assessment of neuropsychological tests would enable widespread, cost‐effective screening for dementia. MethodsA novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects’ neuropsychological tests conducted by the Framingham Heart Study (n= 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants’ demographic characteristics. ResultsAverage area under the curve (AUC) on the held‐out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. DiscussionThe proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.
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This content will become publicly available on July 17, 2026
Developing an accessible dementia assessment tool: Leveraging a residual network, the trail making test, and demographic data
BackgroundThe global burden of Alzheimer's disease and related dementias is rapidly increasing, particularly in low- and middle-income countries where access to specialized healthcare is limited. Neuropsychological tests are essential diagnostic tools, but their administration requires trained professionals, creating screening barriers. Automated computational assessment presents a cost-effective solution for global dementia screening. ObjectiveTo develop and validate an artificial intelligence-based screening tool using the Trail Making Test (TMT), demographic information, completion times, and drawing analysis for enhanced dementia detection. MethodsWe developed: (1) non-image models using demographics and TMT completion times, (2) image-only models, and (3) fusion models. Models were trained and validated on data from the Framingham Heart Study (FHS) (N = 1252), the Long Life Family Study (LLFS) (N = 1613), and the combined cohort (N = 2865). ResultsOur models, integrating TMT drawings, demographics, and completion times, excelled in distinguishing dementia from normal cognition. In the LLFS cohort, we achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 98.62%, with sensitivity/specificity of 87.69%/98.26%. In the FHS cohort, we obtained an AUC of 96.51%, with sensitivity/specificity of 85.00%/96.75%. ConclusionsOur method demonstrated superior performance compared to traditional approaches using age and TMT completion time. Adding images captures subtler nuances from the TMT drawing that traditional methods miss. Integrating the TMT drawing into cognitive assessments enables effective dementia screening. Future studies could aim to expand data collection to include more diverse cohorts, particularly from less-resourced regions.
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- PAR ID:
- 10618452
- Publisher / Repository:
- Sage Journals
- Date Published:
- Journal Name:
- Journal of Alzheimer’s Disease
- ISSN:
- 1387-2877
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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