A new way to detect early dementia in time for intervention

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In a new study, researchers found that if Alzheimer’s dementia is identified early, at the stage of Mild Cognitive Impairment, for instance, the decline in neural functioning can be stabilized or even curtailed in some cases.

They developed automatic machine learning models using language features to identify multiple stages of dementia, including Mild Cognitive Impairment (MCI), possible Alzheimer’s dementia (PoAD), and probable Alzheimer’s dementia (AD), i.e. not fully developed AD.

The findings show that it is possible to identify language changes years prior to developing dementia, which highlights the importance of linguistic analysis for early dementia detection.

The research was conducted by a team at the Queensland University of Technology.

Early, accurate diagnosis is important to enabling clinicians to intervene in time to delay or prevent Alzheimer’s dementia.

Currently, the initial diagnosis is performed with pen-and-paper screening tests such as the Mini Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA).

These traditional tests are normally clinic-based and involve a series of questions and tasks to assess short-term memory, attention, repetition and orientation.

Traditional tests rely on the neurologist’s experience and level of expertise for delivery and assessment and the results are typically affected by the patient’s age (possibility of normal age-related cognitive decline) and level of education.

In the study, the team focused on language ability, because as dementia advanced, a person’s language comprehension and spoken complexity declined.

Dementia severity is linked to a limited vocabulary and increased word repetitions, so researchers can pick up as linguistic biomarkers as dementia progresses.

They analyzed language samples from DementiaBank, a large open-source database of language samples from people with various stages of cognitive impairment and dementia.

They studied 236 language samples from people diagnosed with probable AD, 43 samples from people with MCI, 21 samples belonging to people with possible AD, and 243 from healthy people.

The researchers found people with dementia leaned towards using fewer nouns but more verbs, pronouns, and adjectives as dementia progressed compared to healthy adults.

For instance, they found a noun to verb ratio and verb to noun ratio to be significant in differentiating both AD and MCI from healthy people.

This is interesting, as previous research has revealed that nouns and verbs are learned and activated in different brain regions which could be matched with the area of the brain that is first affected by dementia and help early intervention.

The study was believed to be the first to classify AD, MCI, and PoAD accurately and automatically through machine learning models.

The ultimate aim is to develop a conversational agent or chatbot that could be used remotely to facilitate the initial diagnosis of early-stage dementia as an attempt to replace traditional screening tests.

One author of the study is data science researcher Ahmed Alkenani.

The study is published in IEEE Access.

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